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 Colin Paterson
Tool-supported formal approaches for engineering of adaptive and autonomous systems and processes, probabilistic model checking.
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.
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
Emad Alharbi
Metaheuristics for protein model synthesis from electron-density maps.
Ana Markovic
Multi-language distributed stream processing
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

Recent Publications

Publication details

JournalJournal of Information Security and Applications
DateAccepted/In press - 25 Mar 2022
DateE-pub ahead of print - 2 Apr 2022
DatePublished (current) - 2 Apr 2022
Volume66
Number of pages12
Early online date2/04/22
Original languageEnglish

Abstract

The amount of images with embedded text shared on Online Social Networks (OSNs), such as Twitter or Facebook has been growing in recent years. It is becoming important to analyse the images uploaded into these platforms, as adversaries may spread images with toxic content or misinformation (i.e. spam). Optical character recognition (OCR) systems have been used to detect images with malicious content, where the embedded text gets extracted and classified using machine learning algorithms. However, most existing OCR-based systems are adversary-agnostic models, in which the extracted text from an image is not checked by humans before the classification. Consequently, these fully automated models become vulnerable to minor modifications of images’ pixels or textual content (e.g., character-level perturbations), which do not affect human understanding, but could cause the OCR systems to misrecognise the embedded text. In this paper, we propose an OCR post-correction algorithm to improve the robustness of OCR-based systems against images with perturbed embedded texts. Experimental results showed that our proposed algorithm improves the robustness of three state-of-the-art OCR models with at least 10% against adversarial text images, and it outperforms five spellcheckers in correcting adversarial text. Also, we evaluated the perceptibility of our adversarial images, and this study showed that 91% of the participants were able to correctly recognise the adversarial text images. Additionally, we developed an adversary-aware OCR-based system for detecting adversarial text images using the proposed algorithm, and our evaluation results showed considerable improvement in the performance of an OCR-based system.

Low-code development and model-driven engineering: Two sides of the same coin?

Di Ruscio, D., Kolovos, D., de Lara, J., Pierantonio, A., Tisi, M. & Wimmer, M., 11 Jan 2022, (E-pub ahead of print)

Article in Software and Systems Modeling

Publication details

JournalSoftware and Systems Modeling
DateAccepted/In press - 9 Dec 2021
DateE-pub ahead of print (current) - 11 Jan 2022
Early online date11/01/22
Original languageEnglish

Abstract

The last few years have witnessed a significant growth of so-called low-code development platforms (LCDPs) both in gaining traction on the market and attracting interest from academia. LCDPs are advertised as visual development platforms, typically running on the cloud, reducing the need for manual coding and also targeting non-professional programmers. Since LCDPs share many of the goals and features of model-driven engineering approaches, it is a common point of debate whether low-code is just a new buzzword for model-driven technologies, or whether the two terms refer to genuinely distinct approaches. To contribute to this discussion, in this expert-voice paper, we compare and contrast low-code and model-driven approaches, identifying their differences and commonalities, analysing their strong and weak points, and proposing directions for cross-pollination.

Bibliographical note

Funding Information:
This work has received funding from the Lowcomote project under European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 813884. The work has also been partially funded by the Spanish Ministry of Science (RTI2018-095255-B-I00) and the R&D programme of Madrid (P2018/TCS-4314).

Publisher Copyright:
© 2022, The Author(s).

Publication details

DateSubmitted - 1 May 2022
Original languageEnglish

Abstract

With recent advancements in systems engineering and artificial intelligence, autonomous agents are increasingly being called upon to execute tasks that have normative relevance. These are tasks that directly---and potentially adversely---affect human well-being and demand of the agent a degree of normative-sensitivity and -compliance. Such norms and normative principles are typically of a social, legal, ethical, empathetic, or cultural (`SLEEC') nature. Whereas norms of this type are often framed in the abstract, or as high-level principles, addressing normative concerns in concrete applications of autonomous agents requires the refinement of normative principles into explicitly formulated practical rules.

This paper develops a process for deriving specification rules from a set of high-level norms, thereby bridging the gap between normative principles and operational practice. This enables autonomous agents to select and execute the most normatively favourable action in the intended context premised on a range of underlying relevant normative principles. In the translation and reduction of normative principles to SLEEC rules, we present an iterative process that uncovers normative principles, addresses SLEEC concerns, identifies and resolves SLEEC conflicts, and generates both preliminary and complex normatively-relevant rules, thereby guiding the development of autonomous agents and better positioning them as normatively SLEEC-sensitive or SLEEC-compliant.

Bibliographical note

under review

Publication details

JournalScience of Computer Programming
DateAccepted/In press - 29 Mar 2022
DateE-pub ahead of print (current) - 4 Apr 2022
Early online date4/04/22
Original languageEnglish

Abstract

We present a tool-supported approach to the synthesis, verification, and testing of the control software responsible for the safety of human-robot interaction in manufacturing processes that use collaborative robots. In human-robot collaboration, software-based safety controllers are used to improve operational safety, for example, by triggering shutdown mechanisms or emergency stops to reduce the likelihood of accidents. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to controller developers and certification authorities. Key among these challenges is the need to assure the correctness of safety controllers under explicit (and preferably weak) assumptions. Our integrated synthesis, verification, and test approach is informed by the process, risk analysis, and relevant safety regulations for the target application. Controllers are selected from a design space of feasible controllers according to a set of optimality criteria, are formally verified against correctness criteria, and are translated into executable code and tested in a digital twin. The resulting controller can detect the occurrence of hazards, move the process into a safe state, and, under certain circumstances, return the process to an operational state from which it can resume its original task. We show the effectiveness of our software engineering approach through a case study involving the development of a safety controller for a manufacturing work cell equipped with a collaborative robot.

PRESTO: Predicting System-level Disruptions through Parametric Model Checking

Fang, X., Calinescu, R., Paterson, C. & Wilson, J. C., 10 Mar 2022, (Accepted/In press) 17th International Symposium on Software Engineering for Adaptive and Self-Managing Systems.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Publication details

Title of host publication17th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
DateAccepted/In press - 10 Mar 2022
Original languageEnglish

Abstract

Self-adaptive systems are expected to mitigate disruptions by con-
tinually adjusting their configuration and behaviour. This mitiga-
tion is often reactive. Typically, environmental or internal changes
trigger a system response only after a violation of the system re-
quirements. Despite a broad agreement that prevention is better
than cure in self-adaptation, proactive adaptation methods are
underrepresented within the repertoire of solutions available to
the developers of self-adaptive systems. To address this gap, we
present a work-in-progress approach for the prediction of system-
level disruptions (PRESTO) through parametric model checking.
Intended for use in the analysis step of the MAPE-K (Monitor-
Analyse-Plan-Execute over a shared Knowledge) feedback control
loop of self-adaptive systems, PRESTO comprises two stages. First,
time-series analysis is applied to monitoring data in order to iden-
tify trends in the values of individual system and/or environment
parameters. Next, future non-functional requirement violations are
predicted by using parametric model checking, in order to establish
the potential impact of these trends on the reliability and perfor-
mance of the system. We illustrate the application of PRESTO in a
case study from the autonomous farming domain.

Bibliographical note

© 2022 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

Quantitative Verification with Adaptive Uncertainty Reduction

Alasmari, N., Calinescu, R., Paterson, C. & Mirandola, R., 22 Feb 2022, (E-pub ahead of print)

Article in Journal of Systems and Software

Publication details

JournalJournal of Systems and Software
DateAccepted/In press - 16 Feb 2022
DateE-pub ahead of print (current) - 22 Feb 2022
Volume188
Number of pages19
Early online date22/02/22
Original languageEnglish

Abstract

Stochastic models are widely used to verify whether systems satisfy their reliability, performance and other nonfunctional requirements. However, the validity of the verification depends on how accurately the parameters of these models can be
estimated using data from component unit testing, monitoring, system logs, etc. When insufficient data are available, the models are affected by epistemic parametric uncertainty, the verification results are inaccurate, and any engineering decisions based on them may be invalid. To address these problems, we introduce VERACITY, a tool-supported iterative approach for the efficient and accurate verification of nonfunctional requirements under epistemic parameter uncertainty. VERACITY integrates confidence-interval quantitative verification with a new adaptive uncertainty reduction heuristic that collects additional data about the parameters of the verified model by unit-testing specific system components over a series of verification iterations. VERACITY supports the quantitative verification of discrete-time Markov chains, deciding which components are to be tested in each iteration based on factors that include the sensitivity of the model to variations in the parameters of different components, and the overheads (e.g., time or cost) of unit-testing each of these
components. We show the effectiveness and efficiency of VERACITY by using it for the verification of the nonfunctional requirements of a tele-assistance service-based system and an online shopping web application.

Bibliographical note

© 2022 Elsevier Inc. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

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 proceedingConference 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 conferencePaperpeer-review

Conference

ConferenceSystem Analysis and Modelling: Agility and DevOps
Abbreviated titleSAM
Country/TerritoryJapan
CityFukuoka
Conference date(s)11/10/2112/10/21
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%.

Towards Twin-Driven Engineering: Overview of the State-of-The-Art and Research Directions

Tisi, M., Bruneliere, H., de Lara, J., Di Ruscio, D. & Kolovos, D., 31 Aug 2021, Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings. Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G. & Romero, D. (eds.). Springer Science and Business Media Deutschland GmbH, p. 351-359 9 p. (IFIP Advances in Information and Communication Technology; vol. 630 IFIP).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Publication details

Title of host publicationAdvances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings
DatePublished - 31 Aug 2021
Pages351-359
Number of pages9
PublisherSpringer Science and Business Media Deutschland GmbH
EditorsAlexandre Dolgui, Alain Bernard, David Lemoine, Gregor von Cieminski, David Romero
Original languageEnglish
ISBN (Print)9783030858735

Publication series

NameIFIP Advances in Information and Communication Technology
Volume630 IFIP
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Abstract

Cyber-Physical Systems (CPS) are complex physical systems interacting with a considerable number of distributed computing elements for monitoring, control and management. They are currently becoming larger as Cyber-Physical Systems of Systems (CPSoS), since many industrial companies are transitioning their complex systems of systems to software-intensive solutions in different domains such as production or manufacturing. Following the development and dissemination of DevOps approaches in the Software Engineering world, we propose the Twin-Driven Engineering (TDE) paradigm as a way to upgrade the role of Digital Twins (DT) to become a central point in all the engineering activities on the CPSoS, from design to decommissioning. Since CPSoS can be highly heterogeneous, we rather target the support for producing and maintaining a single integrated virtual representation of the CPSoS (i.e. a System of Twins) on which it is possible to perform global reasoning, analysis and verification. However, such a new paradigm comes with several open research challenges. We provide an overview of the state-of-the-art in key areas related to TDE. We identify under-investigated problems in related work and outline corresponding research directions.

Bibliographical note

Publisher Copyright:
© 2021, IFIP International Federation for Information Processing.

Publication details

JournalSoftware and Systems Modeling
DateAccepted/In press - 9 Jul 2021
DatePublished (current) - 23 Aug 2021
Issue number6
Volume20
Number of pages30
Pages (from-to)1889-1918
Original languageEnglish

Abstract

Open-source model management frameworks such as OCL and ATL tend to focus on manipulating models built atop the Eclipse Modelling Framework (EMF), a de facto standard for domain specific modelling. MATLAB Simulink is a widely used proprietary modelling framework for dynamic systems that is built atop an entirely different technical stack to EMF. To leverage the facilities of open-source model management frameworks with Simulink models, these can be transformed into an EMF-compatible representation. Downsides of this approach include the synchronisation of the native Simulink model and its EMF representation as they evolve; the completeness of the EMF representation, and the transformation cost which can be crippling for large Simulink models. We propose an alternative approach to bridge Simulink models with open-source model management frameworks that uses an “on-the-fly” translation of model management constructs into MATLAB statements. Our approach does not require an EMF representation and can mitigate the cost of the upfront transformation on large models. To evaluate both approaches we measure the performance of a model validation process with Epsilon (a model management framework) on a sample of large Simulink models available on GitHub. Our previous results suggest that, with our approach, the total validation time can be reduced by up to 80%. In this paper, we expand our approach to support the management of Simulink requirements and dictionaries, and we improve the approach to perform queries on collections of model elements more efficiently. We demonstrate the use of the Simulink requirements and dictionaries with a case study and we evaluate the optimisations on collection queries with an experiment that compares the performance of a set of queries on models with different sizes. Our results suggest an improvement by up to 99% on some queries.

Bibliographical note

Funding Information:
The work in this paper was partially supported by Innovate UK and the UK aerospace industry through the HICLASS project (contract #113213) and the EPSRC through the National Productivity Investment Fund in partnership with Rolls-Royce under Grant EP/R512230/1. Richard Paige acknowledges the support of NSERC via the Discovery Grant program.

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

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 proceedingConference 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

Publication details

JournalSoftware and Systems Modeling
DateAccepted/In press - 18 Feb 2021
DatePublished (current) - 25 Mar 2021
Issue number5
Volume20
Number of pages24
Pages (from-to)1689-1712
Original languageEnglish

Abstract

Scalable performance is a major challenge with current model management tools. As the size and complexity of models and model management programs increases and the cost of computing falls, one solution for improving performance of model management programs is to perform computations on multiple computers. In this paper, we demonstrate a low-overhead data-parallel approach for distributed model validation in the context of an OCL-like language. Our approach minimises communication costs by exploiting the deterministic structure of programs and can take advantage of multiple cores on each (heterogeneous) machine with highly configurable computational granularity. Our performance evaluation shows that the implementation is extremely low overhead, achieving a speed up of 24.5× with 26 computers over the sequential case, and 122× when utilising all six cores on each computer.

Bibliographical note

Funding Information:
The work in this paper was supported by the European Commission via the CROSSMINER H2020 Project (Grant #732223).

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

An Architecture for the Development of Distributed Analytics Based on Polystore Events

Zolotas, A., Barmpis, K., Medhat, F., Neubauer, P., Kolovos, D. & Paige, R. F., 4 Mar 2021, Heterogeneous Data Management, Polystores, and Analytics for Healthcare - VLDB Workshops, Poly 2020 and DMAH 2020, Revised Selected Papers. Gadepally, V., Mattson, T., Stonebraker, M., Kraska, T., Wang, F., Luo, G., Kong, J. & Dubovitskaya, A. (eds.). Springer Science and Business Media Deutschland GmbH, p. 54-65 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12633 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Publication details

Title of host publicationHeterogeneous Data Management, Polystores, and Analytics for Healthcare - VLDB Workshops, Poly 2020 and DMAH 2020, Revised Selected Papers
DatePublished - 4 Mar 2021
Pages54-65
Number of pages12
PublisherSpringer Science and Business Media Deutschland GmbH
EditorsVijay Gadepally, Timothy Mattson, Michael Stonebraker, Tim Kraska, Fusheng Wang, Gang Luo, Jun Kong, Alevtina Dubovitskaya
Original languageEnglish
ISBN (Print)9783030710545

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12633 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Abstract

To balance the requirements for data consistency and availability, organisations increasingly migrate towards hybrid data persistence architectures (called polystores throughout this paper) comprising both relational and NoSQL databases. The EC-funded H2020 TYPHON project offers facilities for designing and deploying such polystores, otherwise a complex, technically challenging and error-prone task. In addition, it is nowadays increasingly important for organisations to be able to extract business intelligence by monitoring data stored in polystores. In this paper, we propose a novel approach that facilitates the extraction of analytics in a distributed manner by monitoring polystore queries as these arrive for execution. Beyond the analytics architecture, we presented a pre-execution authorisation mechanism. We also report on preliminary scalability evaluation experiments which demonstrate the linear scalability of the proposed architecture.

Bibliographical note

Funding Information:
Acknowledgements. This work is funded by the TYPHON project (#780251).

Funding Information:
This work is funded by the European Union Horizon 2020 TYPHON project (#780251).

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

High-Availability Clusters: A Taxonomy, Survey, and Future Directions

Somasekaram, P., Calinescu, R. & Buyya, R., 29 Dec 2021, (E-pub ahead of print)

Article in Journal of Systems and Software

Publication details

JournalJournal of Systems and Software
DateAccepted/In press - 22 Dec 2021
DateE-pub ahead of print (current) - 29 Dec 2021
Early online date29/12/21
Original languageEnglish

Bibliographical note

© 2021 Elsevier Inc. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

Uncertainty in Self-adaptive Systems: A Research Community Perspective

Hezavehi, S. M., Weyns, D., Avgeriou, P., Calinescu, R., Mirandola, R. & Perez-Palacin, D., 20 Dec 2021

Article in ACM Transactions on Autonomous and Adaptive Systems

Publication details

JournalACM Transactions on Autonomous and Adaptive Systems
DateAccepted/In press - 1 Sep 2021
DatePublished (current) - 20 Dec 2021
Issue number4
Volume15
Number of pages36
Pages (from-to)1-36
Original languageEnglish

Bibliographical note

© 2021 Copyright held by the owner/author(s)

Publication details

JournalFrontiers in Robotics and AI
DateAccepted/In press - 19 Nov 2021
DatePublished (current) - 10 Dec 2021
Original languageEnglish

Abstract

Digital twins offer a unique opportunity to design, test, deploy, monitor, and control real-world robotic processes. In this paper we present a novel, modular digital twinning framework developed for the investigation of safety within collaborative robotic manufacturing processes. The modular architecture supports scalable representations of user-defined cyber-physical environments, and tools for safety analysis and control. This versatile research tool facilitates the creation of mixed environments of Digital Models, Digital Shadows, and Digital Twins, whilst standardising communication and physical system representation across different hardware platforms. The framework is demonstrated as applied to an industrial case-study focused on the safety assurance of a collaborative robotic manufacturing process. We describe the creation of a digital twin scenario, consisting of individual digital twins of entities in the manufacturing case study, and the application of a synthesised safety controller from our wider work. We show how the framework is able to provide adequate evidence to virtually assess safety claims made against the safety controller using a supporting validation module and testing strategy. The implementation, evidence and safety investigation is presented and discussed, raising exciting possibilities for the use of digital twins in robotic safety assurance.

Bibliographical note

© 2021 Douthwaite, Lesage, Gleirscher, Calinescu, Aitken, Alexander and Law.

Publication details

JournalActa crystallographica. Section D, Structural biology
DateAccepted/In press - 10 Oct 2021
DatePublished (current) - 1 Dec 2021
Issue numberPt 12
Volume77
Number of pages11
Pages (from-to)1591-1601
Original languageEnglish

Abstract

Proteins are macromolecules that perform essential biological functions which depend on their three-dimensional structure. Determining this structure involves complex laboratory and computational work. For the computational work, multiple software pipelines have been developed to build models of the protein structure from crystallographic data. Each of these pipelines performs differently depending on the characteristics of the electron-density map received as input. Identifying the best pipeline to use for a protein structure is difficult, as the pipeline performance differs significantly from one protein structure to another. As such, researchers often select pipelines that do not produce the best possible protein models from the available data. Here, a software tool is introduced which predicts key quality measures of the protein structures that a range of pipelines would generate if supplied with a given crystallographic data set. These measures are crystallographic quality-of-fit indicators based on included and withheld observations, and structure completeness. Extensive experiments carried out using over 2500 data sets show that the tool yields accurate predictions for both experimental phasing data sets (at resolutions between 1.2 and 4.0 Å) and molecular-replacement data sets (at resolutions between 1.0 and 3.5 Å). The tool can therefore provide a recommendation to the user concerning the pipelines that should be run in order to proceed most efficiently to a depositable model.

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 proceedingConference 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 proceedingConference 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

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 proceedingConference 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.

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 proceedingConference 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.

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 proceedingConference 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.

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 proceedingConference 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.

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

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 proceedingConference 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.

Publication details

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

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

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 proceedingConference 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

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 proceedingConference 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 proceedingConference 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 proceedingConference 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 proceedingConference 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 proceedingConference 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 proceedingConference 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 proceedingConference 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 proceedingConference 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 proceedingConference 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

Funded Projects

Responsible Data Science by Design, EUR 956,754.00

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

1/01/2031/12/22

Project: Research project (funded)Research

Description

York Maastricht Partnership Investment Fund
StatusActive
Effective start/end date1/01/2031/12/22

High-Integrity, Complex, Large, Software and Electronic Systems

Bate, I. J., Kolovos, D. & McDermid, J. A.

1/07/1930/06/23

Project: Research project (funded)Research

StatusActive
Effective start/end date1/07/1930/06/23

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 date1/01/1931/12/22

DAISY - Robot-Assisted A&E Triage

Calinescu, R., Habli, I., Hamilton, J., Picardi, C. & Townsend, B.

EPSRC

1/04/2231/03/23

Project: Research project (funded)Research

StatusActive
Effective start/end date1/04/2231/03/23

Reimagining TAS with Disabled Young People

Calinescu, R., Paterson, C. & Townsend, B.

EPSRC

1/03/2228/02/23

Project: Research project (funded)Research

StatusActive
Effective start/end date1/03/2228/02/23

StatusActive
Effective start/end date1/11/2030/04/24

KTP with Rolls Royce 2 - Industry Funding

Kolovos, D.

1/10/1830/04/22

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 date1/10/1830/04/22

KTP With IBM (Innovate)

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

1/04/1831/03/21

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 date1/04/1831/03/21

TYPHON - Polyglot Persistence and Processing of Big Data

Kolovos, D.

EUROPEAN COMMISSION

1/01/1831/12/20

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 date1/01/1831/12/20

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 date1/01/1731/12/19

AcronymScalable Modelling and Model Management on the Cloud
StatusFinished
Effective start/end date1/11/1330/04/16

OSSMETER (EU ICT Bid)

Paige, R. F. & Kolovos, D.

EUROPEAN COMMISSION

1/10/1230/03/15

Project: Research project (funded)Research

StatusFinished
Effective start/end date1/10/1230/03/15

Bridging the Gap Between Programming and Modelling

Paige, R. F.

THE ROYAL SOCIETY

1/03/1829/02/20

Project: Research project (funded)Research

StatusFinished
Effective start/end date1/03/1829/02/20

CyPhERS

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

EUROPEAN COMMISSION

1/07/1328/02/15

Project: Research project (funded)Research

StatusFinished
Effective start/end date1/07/1328/02/15

DSTL PhD Studentship - Radu Calinescu

Calinescu, R. & Paige, R. F.

1/10/1230/09/16

Project: Research project (funded)Research

StatusFinished
Effective start/end date1/10/1230/09/16

COMPASS: Automated Safety Warnings (SESAR)

Paige, R. F.

SESAR JOINT UNDERTAKING

1/04/1130/11/13

Project: Research project (funded)Research

StatusFinished
Effective start/end date1/04/1130/11/13

StatusFinished
Effective start/end date1/02/1031/07/12

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

Paige, R. F.

EPSRC

1/11/0731/10/08

Project: Research project (funded)Research

StatusFinished
Effective start/end date1/11/0731/10/08

Resilient Integration of AI Perception into Trustworthy Autonomous Systems

Calinescu, R.

EPSRC

1/10/2131/03/22

Project: Research project (funded)Research

StatusFinished
Effective start/end date1/10/2131/03/22

Engineering Assured Autonomous Systems

Calinescu, R. & Gerasimou, S.

EPSRC

19/11/1913/08/21

Project: Research project (funded)Research

StatusFinished
Effective start/end date19/11/1913/08/21

DSTL TDS Studentship: Assured Reinforcement Learning

Calinescu, R. & Kudenko, D.

1/10/1330/09/17

Project: Research project (funded)Research

StatusFinished
Effective start/end date1/10/1330/09/17

Cloud Computing for LSCITS

Calinescu, R.

EPSRC

1/05/1231/03/14

Project: Research project (funded)Research

StatusFinished
Effective start/end date1/05/1231/03/14

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