Automated Software Engineering Group

We are a group of 35 researchers in the Department of Computer Science at the University of York, developing ground-breaking methods and tools for automated analysis, design, development, deployment, and management of complex software-intensive systems. We collaborate closely with companies such as Rolls-Royce, IBM, Altran, and Volkswagen on projects co-funded by the European Commission, RCUK, InnovateUK and DSTL.

Members

Professor Dimitris Kolovos
Model-based software engineering, software repository mining and big-data persistence and processing architectures.
Professor Richard Paige
Model-based software engineering, agile development, service-oriented architectures, formal methods, object-oriented programming, systems engineering.
Professor Radu Calinescu
Formal methods for adaptive, autonomic, secure and dependable IT systems, automated, model- and metadata-driven software engineering, formal specification, modelling and verification. Leading the Trustworthy Adaptive and Autonomous Systems & Processes team.
Dr Nicholas Matragkas
Model-based software engineering, software repository mining and software testing.
Dr Simos Gerasimou
Self-adaptive and autonomous systems with a focus on methods that enable dependable system adaptation, runtime quantitative verification, search-based software engineering, model-driven engineering, robotics and artificial intelligence.
Dr Javier Camara Moreno
Software engineering, self-adaptive systems, software architectures, applied formal methods, cyber-physical systems.
Dr Thanos Zolotas
Model-based software engineering, big data architectures
Dr Kostas Barmpis
Model-based software engineering, mining software repositories.
Dr Piotr Dziurzanski
Dr James Harbin
Dr Colin Paterson
Tool-supported formal approaches for engineering of adaptive and autonomous systems and processes, probabilistic model checking.
Dr Sondess Missaoui
Dr Alfa Yohannis
Model-based software engineering, change-based model persistence.
Betty Sanchez
Model-based software engineering, Simulink, reactive modelling workflows.
Seham Alharbi
Assisting developers with APIs.
Adam Homolya
Big-Data Polystore Engineering.
Stephen Wright
Ionut Predoaia
Hybrid graphical/textual domain-specific languages, infrastructure as code.
Sultan Almutairi
Model-based software engineering, model-to-text transformation.
Nikos Fountoulakis
Software repository mining, code repository indexing.
Qurat ul ain Ali
Low-code software engineering
Sorour Jahanbin
Low-code software engineering
Panagiotis Kourouklidis
Low-code software engineering for machine learning
Naif Alasmari
Abdullah Saleh G Albalawi
Khaled Aldheeif
Emad Alharbi
Metaheuristics for protein model synthesis from electron-density maps.
Misael Alpizar Santana
Ana Markovic
Multi-language distributed stream processing
Joshua Riley
Andrew Shepherd
Premathas Somasekaram
Autonomous systems, cloud computing, high availability cluster and grid computing, machine learning, statistical analysis, Bayesian networks.
Ioannis Stefanakos
Formal methods, model-driven software engineering
Mark Rodbert
Carlos Da Silva
Dr Alfonso de la Vega
Dr Ran Wei
Dr Babajide (Jimi) Ogunyomi
Saud Yonbawi
Justin Cooper
Jon Co
Dr Patrick Neubauer
Dr Sina Madani
Bestoon Hussien
Professor Fiona Polack
Dr Horacio Hoyos Rodriguez
Dr James Williams
Faisal Alhwikem
Dr Fady Beshara

Recent Publications

Towards Scalable Validation of Low-Code System Models: Mapping EVL to VIATRA Patterns

Ali, Q. U. A., Horváth, B., Kolovos, D., Barmpis, K. & Horváth, Á., 12 Oct 2021, (E-pub ahead of print) MODELS 2021: Model-Driven Engineering Languages and Systems, proceedings. IEEE, 5 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationMODELS 2021: Model-Driven Engineering Languages and Systems, proceedings
DateE-pub ahead of print - 12 Oct 2021
Number of pages5
PublisherIEEE
Original languageEnglish

Abstract

Adoption of low-code engineering in complex enterprise applications also increases the size of the underlying models. In such cases, the increasing complexity of the applications and the growing size of the underlying artefacts, various scalability
challenges might arise for low-code platforms. Task-specific programming languages, such as OCL and EOL, are tailored to manage the underlying models. Existing model management languages have significant performance impact when it comes to
complex queries operating over large-scale models reaching magnitudes of millions of elements in size. We propose an approach for automatically mapping expressions in Epsilon validation programs to VIATRA graph patterns to make the validation of
large-scale low-code system models scalable by leveraging the incremental execution engine of VIATRA. Finally, we evaluate the performance of the proposed approach on large Java models of the Eclipse source code. Our results show performance speed-up
up to 1481x compared to the sequential execution in Epsilon.

Identification and Optimisation of Type-Level Model Queries

Ali, Q. U. A., Kolovos, D. & Barmpis, K., 11 Oct 2021, (E-pub ahead of print).

Research output: Contribution to conference › Paper › peer-review

Conference

ConferenceSystem Analysis and Modelling: Agility and DevOps
Abbreviated titleSAM
CountryJapan
CityFukuoka
Conference date(s)11/10/21 → 12/10/21
Internet address

Publication details

DateE-pub ahead of print - 11 Oct 2021
Original languageEnglish

Abstract

The main appeal of task-specific model management languages such as ATL, OCL, Epsilon etc. is that they offer tailored syntaxes for the tasks they target, and provide concise first-class support for recurring activities in these tasks. On the
flip side, task-specific model management languages are typically interpreted and are therefore significantly slower than general purpose programming languages (which can be also used to query and modify models) such as Java. While this is not an
issue for smaller models, as models grow in size, naive execution of interpreted model management programs against them can become a scalability bottleneck. In this paper, we demonstrate an architecture for optimisation of model management programs written in languages of the Epsilon platform using static analysis
and program rewriting techniques. The proposed architecture facilitates optimisation of queries that target models of heterogeneous technologies in an orthogonal way. We demonstrate how the proposed architecture is used to identify and optimise typelevel
queries against EMF-based models in the context of EOL programs and EVL validation constraints. We also demonstrate the performance benefits that can be delivered by this form of optimisation through a series of experiments on EMF-based
models. Our experiments have shown performance improvements of up to 99.56%.

Model-Based Development of Engine Control Systems: Experiences and Lessons Learnt

Cooper, J., De La Vega, A., Paige, R. F., Kolovos, D., Michael, B., Brown, C., Sanchez Pina, B. A. & Hoyos Rodriguez, H., 11 Jul 2021, (Accepted/In press) ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems
DateAccepted/In press - 11 Jul 2021
Original languageEnglish

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

Evolutionary-Guided Synthesis of Verified Pareto-Optimal MDP Policies

Gerasimou, S., Camara Moreno, J., Calinescu, R., Alasmari, N., Alhwikem, F. & Fang, X., 31 Aug 2021, (E-pub ahead of print) 36th IEEE/ACM International Conference on Automated Software Engineering.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publication36th IEEE/ACM International Conference on Automated Software Engineering
DateE-pub ahead of print - 31 Aug 2021
Original languageEnglish

Abstract

We present a new approach for synthesising Pareto- optimal Markov decision process (MDP) policies that satisfy complex combinations of quality-of-service (QoS) software requirements. These policies correspond to optimal designs or configurations of software systems, and are obtained by translating MDP models of these systems into parametric Markov chains, and using multi-objective genetic algorithms to synthesise Pareto-optimal parameter values that define the required MDP policies. We use case studies from the service-based systems and robotic control software domains to show that our MDP policy synthesis approach can handle a wide range of QoS requirement combinations unsupported by current probabilistic model checkers. Moreover, for requirement combinations supported by these model checkers, our approach generates better Pareto-optimal policy sets according to established quality metrics.

Model-Driven Simulation-Based Analysis for Multi-Robot Systems

Harbin, J. R., Gerasimou, S., Matragkas, N., Zolotas, A. & Calinescu, R., 1 Aug 2021, (E-pub ahead of print) MODELS 2021: ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationMODELS 2021: ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)
DateAccepted/In press - 12 Jul 2021
DateE-pub ahead of print (current) - 1 Aug 2021
Original languageEnglish

Abstract

Multi-robot systems are increasingly deployed to provide services and accomplish missions whose complexity or cost is too high for a single robot to achieve on its own. Although multi-robot systems offer increased reliability via redundancy and enable the execution of more challenging missions, engineering these systems is very complex. This complexity affects not only the architecture modelling of the robotic team but also the modelling and analysis of the collaborative intelligence enabling the team to complete its mission. Existing approaches for the development of multi-robot applications do not provide a systematic mechanism for capturing these aspects and assessing the robustness of multi-robot systems. We address this gap by introducing ATLAS, a novel model-driven approach supporting the systematic robustness analysis of multi-robot systems in simulation. The ATLAS domain-specific language enables modelling the architecture of the robotic team and its mission, and facilitates the specification of the team’s intelligence. We evaluate ATLAS and demonstrate its effectiveness on two oceanic exploration missions performed by a team of unmanned underwater vehicles developed using the MOOS-IvP robotic simulator.

Bibliographical note

© IEEE, 2021. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

Detection and Mitigation of Rare Subclasses in Deep Neural Network Classifiers

Paterson, C., Calinescu, R. & Picardi, C., 27 Jun 2021, (Accepted/In press) IEEE AI Test 2021 conference.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationIEEE AI Test 2021 conference
DateAccepted/In press - 27 Jun 2021
Original languageEnglish

Abstract

Regions of high-dimensional input spaces that are underrepresented in training datasets reduce machine-learnt classifier performance, and may lead to corner cases and unwanted bias for classifiers used in decision making systems. When these regions belong to otherwise well-represented classes, their presence and negative impact are very hard to identify. We propose an approach for the detection and mitigation of such rare subclasses in deep neural network classifiers. The new approach is underpinned by an easy-to-compute commonality metric that supports the detection of rare subclasses, and comprises methods for reducing the impact of these subclasses during both model training and model exploitation. We demonstrate our approach using two well-known datasets, MNIST's handwritten digits and Kaggle's cats/dogs, identifying rare subclasses and producing models which compensate for subclass rarity. In addition we demonstrate how our run-time approach increases the ability of users to identify samples likely to be misclassified at run-time.

Probabilistic Program Performance Analysis

Stephanakos, I., Calinescu, R. & Gerasimou, S., 1 Jun 2021, (Accepted/In press) EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationEUROMICRO Conference on Software Engineering and Advanced Applications (SEAA 2021)
DateAccepted/In press - 1 Jun 2021
Original languageEnglish

Abstract

We introduce a tool-supported method for the formal analysis of timing, resource use, cost and other quality aspects of computer programs. The new method synthesises a Markov-chain model of the analysed code, computes this quantitative model’s transition probabilities using information from program logs, and employs probabilistic model checking to evaluate the performance properties of interest. Unlike existing solutions, our method can reuse the probabilistic model to accurately predict how the program performance would change if the code ran on a different hardware platform, used a new function library, or had a different usage profile. We show the effectiveness of our method by using it to analyse the performance of Java code from the Apache Commons Math library, the Android messaging app Telegram, and an implementation of the knapsack algorithm.

RiskStructures: A Design Algebra for Risk-Aware Machines

Gleirscher, M., Calinescu, R. & Woodcock, J., 26 May 2021, (E-pub ahead of print)

Article in Formal Aspects of Computing

Publication details

JournalFormal Aspects of Computing
DateAccepted/In press - 11 Mar 2021
DateE-pub ahead of print (current) - 26 May 2021
Number of pages40
Early online date26/05/21
Original languageEnglish

Abstract

Machines, such as mobile robots and delivery drones, incorporate controllers responsible for a task while handling risk (e.g. anticipating and mitigating hazards; and preventing and alleviating accidents). We refer to machines with this capability as risk-aware machines. Risk awareness includes robustness and resilience, and complicates monitoring (i.e., introspection, sensing, prediction), decision making, and control. From an engineering perspective, risk awareness adds a range of dependability requirements to system assurance. Such assurance mandates a correct-by-construction approach to controller design, based on mathematical theory. We introduce RiskStructures, an algebraic framework for risk modelling intended to support the design of safety controllers for risk-aware machines. Using the concept of a risk factor as a modelling primitive, this framework provides facilities to construct, examine, and assure these controllers. We prove desirable algebraic properties of these facilities, and demonstrate their applicability by using them to specify key aspects of safety controllers for risk-aware automated driving and collaborative robots.

Bibliographical note

Funding Information:
Mario Gleirscher was supported in part by the German Research Foundation (DFG) under the Fellowship Grant no. 381212925. Work by Radu Calinescu and Mario Gleirscher was partially supported by the Lloyd's Register Foundation under the Autonomy Assurance International Programme (AAIP) Grant CSI:Cobot. Radu Calinescu was additionally supported by the UKRI Project EP/V026747/1 "Trustworthy Autonomous Systems Node in Resilience". We would like to thank Simon Foster for inspiring discussions on the use of relational specification; Ana Cavalcanti and Cliff Jones for insightful questions about the abstraction, composition, and methodology underlying RiskStructures; James Baxter, AlvaroMiyazawa, and Pedro Ribeiro for enlightening conversations about CSP. We are also thankful to Sam Clark for helpful feedback on an early version of the introductory and closing sections.

Funding Information:
Radu Calinescu was additionally supported by the UKRI Project EP/V026747/1 "Trustworthy Autonomous Systems Node in Resilience". Acknowledgements

Publisher Copyright:
© 2021, The Author(s).

Fast Parametric Model Checking through Model Fragmentation

Fang, X., Calinescu, R., Gerasimou, S. & Alhwikem, F., 7 May 2021, 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE). ACM

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publication2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)
DateAccepted/In press - 18 Dec 2020
DatePublished (current) - 7 May 2021
PublisherACM
Original languageEnglish
ISBN (Print)978-1-6654-0296-5

Abstract

Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational functions of the system and environment parameters. In software engineering, PMC formulae can be used during design, e.g., to analyse the sensitivity of different system architectures to parametric variability, or to find optimal system configurations. They can also be used at runtime, e.g., to check if non-functional requirements are still satisfied after environmental changes, or to select new configurations after such changes. However, current PMC techniques do not scale well to systems with complex behaviour and more than a few parameters. Our paper introduces a fast PMC (fPMC) approach that overcomes this limitation, extending the applicability of PMC to a broader class of systems than previously possible. To this end, fPMC partitions the Markov models that PMC operates with into fragments whose reachability properties are analysed independently, and obtains PMC reachability formulae by combining the results of these fragment analyses. To demonstrate the effectiveness of fPMC, we show how our fPMC tool can analyse three systems (taken from the research literature, and belonging to different application domains) with which current PMC techniques and tools struggle.

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

Maintaining driver attentiveness in shared-control autonomous driving

Calinescu, R., Alasmari, N. & Gleirscher, M., 12 Mar 2021, (Accepted/In press) Software Engineering for Adaptive and Self-Managing Systems. IEEE, (IEEE Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationSoftware Engineering for Adaptive and Self-Managing Systems
DateAccepted/In press - 12 Mar 2021
PublisherIEEE
Original languageEnglish

Publication series

NameIEEE Conference Proceedings
PublisherIEEE

Abstract

We present a work-in-progress approach to improving driver attentiveness in cars provided with automated driving systems. The approach is based on a control loop that monitors the driver’s biometrics (eye movement, heart rate, etc.) and the state of the car; analyses the driver’s attentiveness using a deep neural network; plans driver alerts and changes in the speed of the car using a formally verified controller; and executes this plan using acoustic, visual and haptic actuators. The paper presents (i) the self-adaptive system formed by this monitor-analyse-plan-execute (MAPE) control loop, the car and the monitored driver, and (ii) the use of probabilistic model checking to synthesise the controller for the planning step of the MAPE loop.

Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

Paterson, C., Calinescu, R. & Ashmore, R., 5 Mar 2021, (Accepted/In press)

Article in ACM Computing Surveys

Publication details

JournalACM Computing Surveys
DateAccepted/In press - 5 Mar 2021
Number of pages37
Original languageEnglish

Abstract

Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic and political agendas. Such unprecedented interest is fuelled by a vision of ML applicability extending to healthcare, transportation, defence and other domains of great societal importance. Achieving this vision requires the use of ML in safety-critical applications that demand levels of assurance beyond those needed for current ML applications. Our paper provides a comprehensive survey of the state-of-the-art in the assurance of ML, i.e. in the generation of evidence that ML is sufficiently safe for its intended use. The survey covers the methods capable of providing such evidence at different stages of the machine learning lifecycle, i.e. of the complex, iterative process that starts with the collection of the data used to train an ML component for a system, and ends with the deployment of that component within the system. The paper begins with a systematic presentation of the ML lifecycle and its stages. We then define assurance desiderata for each stage, review existing methods that contribute to achieving these desiderata, and identify open challenges that require further research.

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)

Hawkins, R. D., Paterson, C., Picardi, C., Jia, Y., Calinescu, R. & Habli, I., 3 Feb 2021

Research output: Book/Report › Commissioned report

Publication details

DatePublished - 3 Feb 2021
Original languageEnglish

Reinforcement Learning with Quantitative Verification for Assured Multi-Agent Policies

Riley, J., Calinescu, R., Paterson, C., Kudenko, D. & Banks, A., Feb 2021, 13th International Conference on Agents and Artificial Intelligence.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publication13th International Conference on Agents and Artificial Intelligence
DateAccepted/In press - 12 Nov 2020
DatePublished (current) - Feb 2021
Original languageEnglish

Efficiently Querying Large-Scale Heterogeneous Models

Ali, Q. U. A., Kolovos, D. & Barmpis, K., 27 Oct 2020, Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings. New York, NY, USA: Association for Computing Machinery (ACM), (MODELS '20).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationProceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
DateAccepted/In press - 22 Aug 2020
DatePublished (current) - 27 Oct 2020
PublisherAssociation for Computing Machinery (ACM)
Place of PublicationNew York, NY, USA
Original languageEnglish
ISBN (Print)9781450381352

Publication series

NameMODELS '20
PublisherAssociation for Computing Machinery

Abstract

With the increase in the complexity of software systems, the size and the complexity of underlying models also increases proportionally. In a low-code system, models can be stored in different backend technologies and can be represented in various formats. Tailored high-level query languages are used to query such heterogeneous models, but typically this has a significant impact on performance. Our main aim is to propose optimization strategies that can help to query large models in various formats efficiently. In this paper, we present an approach based on compile-time static analysis and specific query optimizers/translators to improve the performance of complex queries over large-scale heterogeneous models. The proposed approach aims to bring efficiency in terms of query execution time and memory footprint, when compared to the naive query execution for low-code platforms.

Towards Model-Based Development of Decentralised Peer-to-Peer Data Vaults

Yohannis, A., De La Vega, A., Kahrobaei, D. & Kolovos, D., 18 Oct 2020, ACM / IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS). 8 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationACM / IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS)
DateAccepted/In press - 2020
DatePublished (current) - 18 Oct 2020
Number of pages8
Original languageEnglish

Abstract

Using centralised data storage systems has been the standard practice followed by online service providers when managing the personal data of their users.
This method requires users to trust these providers and, to some extent, users are not in full control over their data.
The development of applications around decentralised data vaults, i.e., encrypted storage systems located in user-managed devices, can give this control back to the users as sole owners of the data.
However, the development of such applications is not effort-free, and it requires developers to have specialised knowledge, such as how to deploy secure and peer-to-peer communication systems.
We present Vaultage, a model-based framework that can simplify the development of data vault applications.
We demonstrate its core features through a social network application case study and include some initial evaluation results, showing Vaultage's code generation capabilities and some profiling analysis of the generated network components.

Bibliographical note

© 2020 Association for Computing Machinery. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

To build, or not to build: ModelFlow, a build solution for MDE projects

Sanchez, B., Kolovos, D. & Paige, R., 16 Oct 2020, Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2020. Association for Computing Machinery, Inc, p. 1-11 11 p. (Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationProceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2020
DatePublished - 16 Oct 2020
Pages1-11
Number of pages11
PublisherAssociation for Computing Machinery, Inc
Original languageEnglish
ISBN (Electronic)9781450370196

Publication series

NameProceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2020

Abstract

Conservative execution, end-to-end traceability, and context-aware resource handling are desirable features in model management build processes. Yet, none of the existing MDE-dedicated build tools (e.g. MTC-Flow, MWE2) support such features. An initial investigation of general-purpose build tools (e.g. ANT, Gradle) to assess whether we could build a workflow engine with support for these desirable features on top of it revealed limitations that could act as roadblocks for our work. As such, we decided to design and implement a new MDE-focused build tool (ModelFlow) from scratch to avoid being constrained by assumptions and technical constraints of these tools. We evaluated whether this decision was sensible by attempting to replicate its behaviour with Gradle in a typical model-driven engineering scenario. The evaluation highlighted scenarios where Gradle could not be extended to achieve the desirable behaviour which validates the decision to not base ModelFlow on top of it.

Polyglot and Distributed Software Repository Mining with Crossflow

Matragkas, N., Kolovos, D., Barmpis, K., Neubauer, P. & Paige, R., Oct 2020, MSR '20: Proceedings of the 17th International Conference on Mining Software Repositories. p. 374-384 11 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationMSR '20: Proceedings of the 17th International Conference on Mining Software Repositories
DatePublished - Oct 2020
Pages374-384
Number of pages11
Original languageEnglish

Automatic generation of UML profile graphical editors for Papyrus

Wei, R., Zolotas, A., Hoyos Rodriguez, H., Gerasimou, S., Kolovos, D. S. & Paige, R. F., 11 Aug 2020

Article in Software and Systems Modeling

Publication details

JournalSoftware and Systems Modeling
DateAccepted/In press - 10 Jun 2020
DatePublished (current) - 11 Aug 2020
Number of pages24
Original languageEnglish

Abstract

UML profiles offer an intuitive way for developers to build domain-specific modelling languages by reusing and extending UML concepts. Eclipse Papyrus is a powerful open-source UML modelling tool which supports UML profiling. However, with power comes complexity, implementing non-trivial UML profiles and their supporting editors in Papyrus typically requires the developers to handcraft and maintain a number of interconnected models through a loosely guided, labour-intensive and error-prone process. We demonstrate how metamodel annotations and model transformation techniques can help manage the complexity of Papyrus in the creation of UML profiles and their supporting editors. We present Jorvik, an open-source tool that implements the proposed approach. We illustrate its functionality with examples, and we evaluate our approach by comparing it against manual UML profile specification and editor implementation using a non-trivial enterprise modelling language (Archimate) as a case study. We also perform a user study in which developers are asked to produce identical editors using both Papyrus and Jorvik demonstrating the substantial productivity and maintainability benefits that Jorvik delivers.

Bibliographical note

© The Author(s) 2020

Efficient Generation of Graphical Model Views via Lazy Model-to-Text Transformation

Kolovos, D., De La Vega, A. & Cooper, J., 13 Jul 2020, (Accepted/In press) ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS ’20).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS ’20)
DateAccepted/In press - 13 Jul 2020
Original languageEnglish

Abstract

Producing graphical views from software and system models is often desirable for communication and comprehension purposes, even when graphical model editing capabilities are not required -- because the preferred editable concrete syntax of the models is text-based, or for models extracted via reverse engineering. To support such scenarios, we present a novel approach for efficient rule-based generation of transient graphical views from models using lazy model-to-text transformation, and an implementation of the proposed approach in the form of an open-source Eclipse plugin named Picto. Picto builds on top of mature visualisation software such as Graphviz and PlantUML and supports, among others, composite views, layers, and multi-model visualisation. We illustrate how Picto can be used to produce various forms of graphical views such as node-edge diagrams, tables and sequence-like diagrams, and we demonstrate the efficiency benefits of lazy view generation approach against batch model-to-text transformation for generating views from large models.

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

Supporting Robotic Software Migration Using Static Analysis and Model-Driven Engineering

Gerasimou, S., Wood, S., Matragkas, N., Kolovos, D. & Paige, R. F., 13 Jul 2020, (Accepted/In press) ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS ’20).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS ’20)
DateAccepted/In press - 13 Jul 2020
Original languageEnglish

Abstract

The wide use of robotic systems contributed to developing robotic software highly coupled to the hardware platform running the robotic system. Due to increased maintenance cost or changing business priorities, the robotic hardware is infrequently upgraded, thus increasing the risk for technology stagnation. Reducing this risk entails migrating the system and its software to a new hardware platform. Conventional software engineering practices such as complete re-development and code-based migration, albeit useful in mitigating these obsolescence issues, they are time-consuming and overly expensive. Our RoboSMi model-driven approach supports the migration of the software controlling a robotic system between hardware platforms. First, RoboSMi executes static analysis on the robotic software of the source hardware platform to identify platform-dependent and platform-agnostic software constructs. By analysing a model that expresses the architecture of robotic components on the target platform, RoboSMi establishes the hardware configuration of those components and suggests software libraries for each component whose execution will enable the robotic software to control the components. Finally, RoboSMi through code-generation produces software for the target platform and indicates areas that require manual intervention by robotic engineers to complete the migration. We evaluate the applicability of RoboSMi and analyse the level of automation and performance provided from its use by migrating two robotic systems deployed for an environmental monitoring and a line following mission from a Propeller Activity Board to an Arduino Uno.

Guest editorial to the special section on MODELS 2018

Wąsowski, A., Paige, R. F. & Haugen, Ø., 18 May 2020

Editorial in Software and Systems Modeling

Publication details

JournalSoftware and Systems Modeling
DateAccepted/In press - 1 Jan 2020
DatePublished (current) - 18 May 2020
Original languageEnglish

Grand challenges in model-driven engineering: an analysis of the state of the research

Bucchiarone, A., Cabot, J., Paige, R. F. & Pierantonio, A., 1 Jan 2020

Article in Software and Systems Modeling

Publication details

JournalSoftware and Systems Modeling
DateAccepted/In press - 4 Dec 2019
DatePublished (current) - 1 Jan 2020
Issue number1
Volume19
Number of pages9
Pages (from-to)5-13
Original languageEnglish

Abstract

In 2017 and 2018, two events were held—in Marburg, Germany, and San Vigilio di Marebbe, Italy, respectively—focusing on an analysis of the state of research, state of practice, and state of the art in model-driven engineering (MDE). The events brought together experts from industry, academia, and the open-source community to assess what has changed in research in MDE over the last 10 years, what challenges remain, and what new challenges have arisen. This article reports on the results of those meetings, and presents a set of grand challenges that emerged from discussions and synthesis. These challenges could lead to research initiatives for the community going forward.

Bibliographical note

© The Author(s) 2020

Empirical Analysis of 1-edit Degree Patches in Syntax-Based Automatic Program Repair

Dziurzanski, P., Gerasimou, S., Kolovos, D. & Matragkas, N., 20 Mar 2020, (Accepted/In press) IEEE Congress on Evolutionary Computation.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationIEEE Congress on Evolutionary Computation
DateAccepted/In press - 20 Mar 2020
Original languageEnglish

Abstract

In this paper, software patches modifying a single line (aka 1-edit degree patches) of buggy Java open-source projects have been generated automatically using computational search and experimentally evaluated. We carried out the presumably largest to date experiment related to 1-edit degree patches, consisting of almost 27,000 computational jobs upper bounded with 107,000 computational hours. Our experiments show the benefits and drawbacks of such kind of patches. In particular, the search space size has been shown to be reduced by several orders of magnitude. The volume of tests that can be filtered out without any negative impact while generating 1-edit degree patches has been increased by about 97%.
Finally, the effectiveness of finding 1-edit plausible patches is compared with multi-line plausible patches found with state-of-the-art syntax-based Automatic Program Repair tools. It is shown that despite patching fewer bugs in total, 1-edit degree patches have potential to patch some extra bugs.

Importance-Driven Deep Learning System Testing

Gerasimou, S., Eniser, H. F. & Sen, A., 2020, 42nd International Conference on Software Engineering.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publication42nd International Conference on Software Engineering
DateAccepted/In press - 9 Dec 2019
DatePublished (current) - 2020
Original languageEnglish

Abstract

Deep Learning (DL) systems are key enablers for engineering intelligent applications due to their ability to solve complex tasks such as image recognition and machine translation. Nevertheless, using DL systems in safety- and security-critical applications requires to provide testing evidence for their dependable operation. Recent research in this direction focuses on adapting testing criteria from traditional software engineering as a means of increasing confidence for their correct behaviour. However, they are inadequate in capturing the intrinsic properties exhibited by these systems. We bridge this gap by introducing DeepImportance, a systematic testing methodology accompanied by an Importance-Driven (IDC) test adequacy criterion for DL systems. Applying IDC enables to establish a layer-wise functional understanding of the importance of DL system components and use this information to guide the generation of semantically-diverse test sets. Our empirical evaluation on several DL systems, across multiple DL datasets and with state-of-the-art adversarial generation techniques demonstrates the usefulness and effectiveness of DeepImportance and its ability to guide the engineering of more robust DL systems.

Intelligent Run-Time Partitioning of Low-Code System Models

Jahanbin, S., Kolovos, D. & Gerasimou, S., 2020, Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings. New York, NY, USA: Association for Computing Machinery (ACM), (MODELS '20).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationProceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
DatePublished - 2020
PublisherAssociation for Computing Machinery (ACM)
Place of PublicationNew York, NY, USA
Original languageEnglish
ISBN (Print)9781450381352

Publication series

NameMODELS '20
PublisherAssociation for Computing Machinery

Abstract

Over the last 2 decades, several dedicated languages have been proposed to support model management activities such as model validation, transformation, and code generation. As software systems become more complex, underlying system models grow proportionally in both size and complexity. To keep up, model management languages and their execution engines need to provide increasingly more sophisticated mechanisms for making the most efficient use of the available system resources. Efficiency is particularly important when model-driven technologies are used in the context of low-code platforms where all model processing happens in pay-per-use cloud resources. In this paper, we present our vision for an approach that leverages sophisticated static program analysis of model management programs to identify, load, process and transparently discard relevant model partitions - instead of naively loading the entire models into memory and keeping them loaded for the duration of the execution of the program. In this way, model management programs will be able to process system models faster with a reduced memory footprint, and resources will be freed that will allow them to accommodate even larger models.

Interval Change-Point Detection for Runtime Probabilistic Model Checking

Zhao, X., Calinescu, R., Gerasimou, S., Robu, V. & Flynn, D., 2020, 35th IEEE/ACM International Conference on Automated Software Engineering.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publication35th IEEE/ACM International Conference on Automated Software Engineering
DateAccepted/In press - 30 Jul 2020
DatePublished (current) - 2020
Original languageEnglish

HaiQ: Synthesis of Software Design Spaces with Structural and Probabilistic Guarantees

Camara Moreno, J., 2020, Proceedings of the 8th International Conference on Formal Methods in Software Engineering (FormaliSE 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publication Proceedings of the 8th International Conference on Formal Methods in Software Engineering (FormaliSE 2020)
DatePublished - 2020
Original languageEnglish

Model-Based Analysis of Microservice Resiliency Patterns

Camara Moreno, J., Garlan, D., Nabor, M. & Mendes Aderaldo, C., 2020, Proceedings of the IEEE International Conference on Software Architecture (ICSA 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationProceedings of the IEEE International Conference on Software Architecture (ICSA 2020)
DatePublished - 2020
Original languageEnglish

Quantitative Verification-Aided Machine Learning: A Tandem Approach for Architecting Self-Adaptive IoT Systems

Camara Moreno, J., Muccini, H. & Vaidhyanathan, K., 2020, Proceedings of the IEEE International Conference on Software Architecture (ICSA 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationProceedings of the IEEE International Conference on Software Architecture (ICSA 2020)
DatePublished - 2020
Original languageEnglish

Reasoning about When to Provide Explanation for Human-in-the-loop Self-Adaptive Systems

Camara Moreno, J., Garlan, D., Schmerl, B. & Li, N., 2020, Proceedings of the 2020 IEEE Conference on Autonomic Computing and Self-organizing Systems (ACSOS 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationProceedings of the 2020 IEEE Conference on Autonomic Computing and Self-organizing Systems (ACSOS 2020)
DatePublished - 2020
Original languageEnglish

Software Architecture and Task Plan Co-Adaptation for Mobile Service Robots

Camara Moreno, J., Garlan, D. & Schmerl, B., 2020, Proceedings of the 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationProceedings of the 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2020)
DatePublished - 2020
Original languageEnglish

Reusable textual styles for domain-specific modeling languages

Neubauer, P., Bill, R., Kolovos, D., Paige, R. & Wimmer, M., 6 Dec 2019

Conference article in CEUR Workshop Proceedings

Publication details

JournalCEUR Workshop Proceedings
DatePublished - 6 Dec 2019
Volume2513
Number of pages14
Pages (from-to)67-80
Original languageEnglish

Abstract

Domain-specific languages enable concise and precise formalization of domain concepts and promote direct employment by domain experts. Therefore, syntactic constructs are introduced to empower users to associate concepts and relationships with visual textual symbols. Model-based language engineering facilitates the description of concepts and relationships in an abstract manner. However, concrete representations are commonly attached to abstract domain representations, such as annotations in metamodels, or directly encoded into language grammar and thus introduce redundancy between metamodel elements and grammar elements. In this work we propose an approach that enables autonomous development and maintenance of domain concepts and textual language notations in a distinctive and metamodel-agnostic manner by employing style models containing grammar rule templates and injection-based property selection. We provide an implementation and showcase the proposed notationspecification language in a comparison with state of the art practices during the creation of notations for an executable domain-specific modeling language based on the Eclipse Modeling Framework and Xtext.

Bibliographical note

© 2019 The Authors.

Modelflow: Towards reactive model management workflows

Sanchez, B., Kolovos, D. S. & Paige, R., 20 Oct 2019, DSM 2019 - Proceedings of the 17th ACM SIGPLAN International Workshop on Domain-Specific Modeling, co-located with SPLASH 2019. Rossi, M. & Sprinkle, J. (eds.). Association for Computing Machinery, Inc, p. 30-39 10 p. (DSM 2019 - Proceedings of the 17th ACM SIGPLAN International Workshop on Domain-Specific Modeling, co-located with SPLASH 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationDSM 2019 - Proceedings of the 17th ACM SIGPLAN International Workshop on Domain-Specific Modeling, co-located with SPLASH 2019
DatePublished - 20 Oct 2019
Pages30-39
Number of pages10
PublisherAssociation for Computing Machinery, Inc
EditorsMatti Rossi, Jonathan Sprinkle
Original languageEnglish
ISBN (Electronic)9781450369848

Publication series

NameDSM 2019 - Proceedings of the 17th ACM SIGPLAN International Workshop on Domain-Specific Modeling, co-located with SPLASH 2019

Abstract

In this paper we propose a domain specific language that enables the description and execution of model management workflows. Our language declares tasks and resources involved in a multi-step model management process and resolves the execution behaviour and order based on dependencies among these components. We describe the abstract and a concrete syntax of the language along with its execution semantics. Then, we demonstrate how the language interpreter can orchestrate and execute a selection model management tasks through a case study of a workflow that generates a graphical editor from a metamodel.

On the challenges of model decorations for capturing complex metadata

Hoyos Rodriguez, H., Zolotas, A., Kolovos, D. & Paige, R. F., Sep 2019, Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019. Burgueno, L., Burgueno, L., Pretschner, A., Voss, S., Chaudron, M., Kienzle, J., Volter, M., Gerard, S., Zahedi, M., Bousse, E., Rensink, A., Polack, F., Engels, G. & Kappel, G. (eds.). Institute of Electrical and Electronics Engineers Inc., p. 347-353 7 p. 8904658. (Proceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationProceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019
DatePublished - Sep 2019
Pages347-353
Number of pages7
PublisherInstitute of Electrical and Electronics Engineers Inc.
EditorsLoli Burgueno, Loli Burgueno, Alexander Pretschner, Sebastian Voss, Michel Chaudron, Jorg Kienzle, Markus Volter, Sebastien Gerard, Mansooreh Zahedi, Erwan Bousse, Arend Rensink, Fiona Polack, Gregor Engels, Gerti Kappel
Original languageEnglish
ISBN (Electronic)9781728151250

Publication series

NameProceedings - 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion, MODELS-C 2019

Abstract

Model decorations have proven useful as an extension mechanism to provide bespoke language extensions for particular scenarios in the language's domain. However, the current state of the art has only explored extension mechanisms that allow capturing basic metadata, e.g. additional attributes. In this paper we explore the challenges encountered when decorations must capture more complex metadata, in particular metadata that targets model management operations. Additionally, we provide an initial take on these challenges through automated language extension generation. The generated extensions provide enhanced model decoration capabilities that can support metadata of higher complexity.

On-the-fly Translation and Execution of OCL-like Queries on Simulink Models

Sanchez Pina, B. A., Zolotas, A., Hoyos Rodriguez, H., Kolovos, D. & Paige, R. F., 19 Jun 2019, (Accepted/In press) Proceedings of the ACM/IEEE 22th International Conference on Model Driven Engineering Languages and Systems.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationProceedings of the ACM/IEEE 22th International Conference on Model Driven Engineering Languages and Systems
DateAccepted/In press - 19 Jun 2019
Original languageEnglish

Towards systematic engineering of collaborative heterogeneous robotic systems

Gerasimou, S., Matragkas, N. & Calinescu, R., 27 May 2019, 2019 IEEE/ACM 2nd International Workshop on Robotics Software Engineering (RoSE). IEEE, p. 25-28 4 p.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publication2019 IEEE/ACM 2nd International Workshop on Robotics Software Engineering (RoSE)
DatePublished - 27 May 2019
Pages25-28
Number of pages4
PublisherIEEE
Original languageEnglish
ISBN (Electronic)9781728122496

Crossflow: A framework for distributed mining of software repositories

Kolovos, D., Neubauer, P., Barmpis, K., Matragkas, N. & Paige, R., 1 May 2019, Proceedings - 2019 IEEE/ACM 16th International Conference on Mining Software Repositories, MSR 2019. IEEE Computer Society Press, p. 155-159 5 p. 8816734. (IEEE International Working Conference on Mining Software Repositories; vol. 2019-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationProceedings - 2019 IEEE/ACM 16th International Conference on Mining Software Repositories, MSR 2019
DatePublished - 1 May 2019
Pages155-159
Number of pages5
PublisherIEEE Computer Society Press
Original languageEnglish
ISBN (Electronic)9781728134123

Publication series

NameIEEE International Working Conference on Mining Software Repositories
Volume2019-May
ISSN (Print)2160-1852
ISSN (Electronic)2160-1860

Abstract

Large-scale software repository mining typically requires substantial storage and computational resources, and often involves a large number of calls to (rate-limited) APIs such as those of GitHub and StackOverflow. This creates a growing need for distributed execution of repository mining programs to which remote collaborators can contribute computational and storage resources, as well as API quotas (ideally without sharing API access tokens or credentials). In this paper we introduce Crossflow, a novel framework for building distributed repository mining programs. We demonstrate how Crossflow can delegate mining jobs to remote workers and cache their results, and how workers can implement advanced behaviour such as load balancing and rejecting jobs they cannot perform (e.g. due to lack of space, credentials for a specific API).

Type inference in flexible model-driven engineering using classification algorithms

Zolotas, A., Matragkas, N., Devlin, S., Kolovos, D. S. & Paige, R. F., 8 Feb 2019

Article in International Journal on Software & Systems Modelling

Publication details

JournalInternational Journal on Software & Systems Modelling
DateAccepted/In press - 11 Jan 2018
DateE-pub ahead of print - 23 Jan 2018
DatePublished (current) - 8 Feb 2019
Issue number1
Volume18
Number of pages23
Pages (from-to)345-366
Early online date23/01/18
Original languageEnglish

Abstract

Flexible or bottom-up model-driven engineering (MDE) is an emerging approach to domain and systems modelling. Domain experts, who have detailed domain knowledge, typically lack the technical expertise to transfer this knowledge using traditional MDE tools. Flexible MDE approaches tackle this challenge by promoting the use of simple drawing tools to increase the involvement of domain experts in the language definition process. In such approaches, no metamodel is created upfront, but instead the process starts with the definition of example models that will be used to infer the metamodel. Pre-defined metamodels created by MDE experts may miss important concepts of the domain and thus restrict their expressiveness. However, the lack of a metamodel, that encodes the semantics of conforming models has some drawbacks, among others that of having models with elements that are unintentionally left untyped. In this paper, we propose the use of classification algorithms to help with the inference of such untyped elements. We evaluate the proposed approach in a number of random generated example models from various domains. The correct type prediction varies from 23 to 100% depending on the domain, the proportion of elements that were left untyped and the prediction algorithm used.

Machine learning meets quantitative planning: enabling self-adaptation in autonomous robots

Camara Moreno, J., Jamshidi, P., Kästner, C., Garlan, D. & Schmerl, B., May 2019, Proceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2019) .

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publicationProceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2019)
DatePublished - May 2019
Original languageEnglish

Socio-Cyber-Physical Systems: Models, Opportunities, Open Challenges

Calinescu, R. C., Camara Moreno, J. & Paterson, C., 2019, (Accepted/In press) 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Publication details

Title of host publication5th International Workshop on Software Engineering for Smart Cyber-Physical Systems
DateAccepted/In press - 2019
Original languageEnglish

Abstract

Almost without exception, cyber-physical systems operate alongside, for the benefit of, and supported by humans. Unsurprisingly, disregarding their social aspects during
development and operation renders these systems ineffective. In this paper, we explore approaches to modelling and reasoning about the human involvement in socio-cyber-physical systems (SCPS). To provide an unbiased perspective, we describe both the opportunities afforded by the presence of human agents, and the challenges associated with ensuring that their modelling is sufficiently accurate to support decision making during SCPS development and, if applicable, at run-time. Using SCPS examples from emergency management and assisted living, we illustrate how recent advances in stochastic modelling, analysis and synthesis can be used to exploit human observations about the impact of natural and man-made disasters, and to support the efficient provision of assistive care.

Synthesizing tradeoff spaces with quantitative guarantees for families of software systems

Camara Moreno, J., Garlan, D. & Schmerl, B., 2019

Article in Journal of Systems and Software

Publication details

JournalJournal of Systems and Software
DatePublished - 2019
Original languageEnglish

Funded Projects

Responsible Data Science by Design, EUR 956,754.00

Kahrobaei, D., Kolovos, D. & Matragkas, N.

1/01/20 → 31/12/22

Project: Research project (funded) › Research

Description

York Maastricht Partnership Investment Fund
StatusActive
Effective start/end date1/01/20 → 31/12/22

Lowcomote: Training the Next Generation of Experts in Scalable Low-Code Engineering Platforms

Kolovos, D.

EUROPEAN COMMISSION

1/01/19 → 31/12/22

Project: Research project (funded) › Research

Description

Marie Skłodowska-Curie training network of 15 Early Stage Researchers across Europe investigating aspects of scalability in low-code software engineering platforms. Network members include British Telecom, Intecs, B2T Concept, CLMS, IncQuery Labs and the Universities of Nantes (IMT), Madrid (Autonoma), L'Aquila and (TU) Wien.
StatusActive
Effective start/end date1/01/19 → 31/12/22

Resilient Integration of AI Perception into Trustworthy Autonomous Systems

Calinescu, R.

EPSRC

1/10/21 → 31/03/22

Project: Research project (funded) › Research

StatusActive
Effective start/end date1/10/21 → 31/03/22

UKRI Trustworthy Autonomous Systems Node in Resilience

Calinescu, R., Arvind, T., Cavalcanti, A. L. C., Habli, I., Thomas, A. P. & Wilson, J. C.

EPSRC

1/11/20 → 30/04/24

Project: Research project (funded) › Research

StatusActive
Effective start/end date1/11/20 → 30/04/24

KTP with Rolls Royce 2 - Industry Funding

Kolovos, D.

1/10/18 → 30/09/21

Project: Research project (funded) › Research

Description

Knowledge Transfer Partnership with Rolls-Royce on Model-Based Development of Aerospace Systems, co-funded by InnovateUK
StatusFinished
Effective start/end date1/10/18 → 30/09/21

KTP With IBM (Innovate)

Kolovos, D., Manandhar, S. & Paige, R. F.

1/04/18 → 31/03/21

Project: Research project (funded) › Research

Description

Knowledge Transfer Partnership with IBM UK on automated knowledge extraction and re-engineering of financial planning spreadsheets, co-funded by InnovateUK
StatusFinished
Effective start/end date1/04/18 → 31/03/21

TYPHON - Polyglot Persistence and Processing of Big Data

Kolovos, D.

EUROPEAN COMMISSION

1/01/18 → 31/12/20

Project: Research project (funded) › Research

Description

Horizon 2020 project on polyglot (relational/document/graph) data persistence and processing architectures with Volkswagen, GMV, Alpha Bank, OTE, the Open Group, and the Universities of L'Aquila, Edge Hill, Namur and Amsterdam (CWI)
StatusFinished
Effective start/end date1/01/18 → 31/12/20

CROSSMINER Developer-Centric Knowledge Mining from Large Open-Source Software Repositories

Kolovos, D. & Paige, R. F.

EUROPEAN COMMISSION

1/01/17 → 31/12/19

Project: Research project (funded) › Research

Description

Horizon 2020 project on knowledge mining from open-source software repositories with the Eclipse Foundation, the Open Group, OW2, Bitergia, FrontEndArt, Softeam, Unparallel Innovation, Castalia and the Universities of L'Aquila, Athens (AUEB), Amsterdam (CWI), and Edge Hill
StatusFinished
Effective start/end date1/01/17 → 31/12/19

Scalable Modelling and Model Management on the Cloud: MONDO

Kolovos, D., Paige, R. F. & Rose, L. M.

EUROPEAN COMMISSION

1/11/13 → 30/04/16

Project: Research project (funded) › Research

AcronymScalable Modelling and Model Management on the Cloud
StatusFinished
Effective start/end date1/11/13 → 30/04/16

OSSMETER (EU ICT Bid)

Paige, R. F. & Kolovos, D.

EUROPEAN COMMISSION

1/10/12 → 30/03/15

Project: Research project (funded) › Research

StatusFinished
Effective start/end date1/10/12 → 30/03/15

Bridging the Gap Between Programming and Modelling

Paige, R. F.

THE ROYAL SOCIETY

1/03/18 → 29/02/20

Project: Research project (funded) › Research

StatusFinished
Effective start/end date1/03/18 → 29/02/20

CyPhERS

McDermid, J. A. & Paige, R. F.

EUROPEAN COMMISSION

1/07/13 → 28/02/15

Project: Research project (funded) › Research

StatusFinished
Effective start/end date1/07/13 → 28/02/15

DSTL PhD Studentship - Radu Calinescu

Calinescu, R. & Paige, R. F.

1/10/12 → 30/09/16

Project: Research project (funded) › Research

StatusFinished
Effective start/end date1/10/12 → 30/09/16

COMPASS: Automated Safety Warnings (SESAR)

Paige, R. F.

SESAR JOINT UNDERTAKING

1/04/11 → 30/11/13

Project: Research project (funded) › Research

StatusFinished
Effective start/end date1/04/11 → 30/11/13

MADES: Model-based methods and tools for Avionics and surveillance embedded Systems

Paige, R. F.

EUROPEAN COMMISSION

1/02/10 → 31/07/12

Project: Research project (funded) › Research

StatusFinished
Effective start/end date1/02/10 → 31/07/12

Development of Collaborations with the Weizmann Institute of Science and IBM Haifa

Paige, R. F.

EPSRC

1/11/07 → 31/10/08

Project: Research project (funded) › Research

StatusFinished
Effective start/end date1/11/07 → 31/10/08

Engineering Assured Autonomous Systems

Calinescu, R. & Gerasimou, S.

EPSRC

19/11/19 → 13/10/21

Project: Research project (funded) › Research

StatusFinished
Effective start/end date19/11/19 → 13/10/21

DSTL TDS Studentship: Assured Reinforcement Learning

Calinescu, R. & Kudenko, D.

1/10/13 → 30/09/17

Project: Research project (funded) › Research

StatusFinished
Effective start/end date1/10/13 → 30/09/17

Cloud Computing for LSCITS

Calinescu, R.

EPSRC

1/05/12 → 31/03/14

Project: Research project (funded) › Research

StatusFinished
Effective start/end date1/05/12 → 31/03/14

Automatic Repair Of Natural Source Code (MANATEE)

Matragkas, N.

Project: Research project (funded) › Research

StatusNot started

Secure and Safe Multi-Robot Systems

Matragkas, N. & Gerasimou, S.

Project: Research project (funded) › Research

Short titleSESAME
StatusNot started