Journals

• Istvan, D., Aslam, K., Malavolta, I. and Lago, P. “Collaborative Model-Driven Software Engineering – A Systematic Survey of Practices and Needs in Industry” under minor review at Journal of Systems and Software.
  Abstract
  The engineering of modern software-intensive systems is carried out in collaboration among stakeholders with specialized expertise. The complexity of such systems often also necessitates employing more rigorous approaches, such as Model-Driven Software Engineering (MDSE). Collaborative MDSE is the combination of the two disciplines, with its specific opportunities and challenges. The rapid expansion and maturation of the field started attracting tool builders from outside of academia. However, available systematic studies on collaborative MDSE focus exclusively on mapping academic research and fail to identify how academic research aligns with industry practices and needs. To address this shortcoming, we have carried out a mixed-method survey on the practices and needs concerning collaborative MDSE. First, we carried out a qualitative survey in two focus group sessions, interviewing seven industry experts. Second, based on the results of the interviews, we constructed a questionnaire and carried out a questionnaire survey with 41 industry expert participants. In this paper, we report the results of our study, investigate the alignment of academic research with the needs of practitioners, and suggest directions on research and development of the supporting techniques of collaborative MDSE

• Aslam, K., Cleophas, L., Schiffelers, R. and van den Brand, M. “Interface protocol inference to aid understanding legacy software components. Software and Systems Modeling” (SoSym 2020), pp. 1-22.
  Abstract
  High-tech companies are struggling today with the maintenance of legacy software. Legacy software is vital to many organizations as it contains important business logic. To facilitate the maintenance of legacy software, a comprehensive understanding of the software’s behavior is essential. In terms of component-based software engineering, it is necessary to completely understand the behavior of components in relation to their interfaces, i.e., their interface protocols, and to preserve this behavior during the maintenance activities of the components. For this purpose, we present an approach to infer the interface protocols of software components from the behavioral models of those components, learned by a black box technique called active (automata) learning. To validate the learned results, we applied our approach to the software components developed with model-based engineering so that equivalence can be checked between the learned models and the reference models, ensuring the behavioral relations are preserved. Experimenting with components having reference models and performing equivalence checking builds confidence that applying active learning techniques to reverse engineer legacy software components, for which no reference models are available, will also yield correct results. To apply our approach in practice, we present an automated framework for conducting active learning on a large set of components and deriving their interface protocols. Using the framework, we validated our methodology by applying active learning on 202 industrial software components, out of which, interface protocols could be successfully derived for 156 components within our given timebound of 1h for each component

Conferences

• Istvan, D., Aslam, K., Faridmoayer, S., Malavolta, I., Syriani, E. and Lago, P. “Collaborative Model-Driven Software Engineering: A Systematic Update” in 2021 ACM/IEEE International Conference on Model-Driven Languages and Systems (MODELS 2021).
  Abstract
  Current software engineering practices rely on highly heterogeneous and distributed teams working in a collaborative setting. Between 2013–2020, the publication output in the field of collaborative Model-Driven Software Engineering (MDSE) has significantly increased. However, the only systematic mapping study available is limited to studies published until 2015. In this paper, we provide an update on that study for the complementing 2016–2020 period, and report the latest results, challenges, and trends. Our analysis led to selecting 29 clusters of 54 new peer-reviewed publications on collaborative MDSE. Based on the novel developments in the field, we have extended and improved the original classification framework, making it applicable to recent and future research contributions on collaborative MDSE. The insights in this paper relate to the changing trends in the field and present new relevant information.

• Hendriks, D., Aslam, K. “A Systematic Approach for Interfacing Component-Based Software with an Active Automata Learning Tool. ISoLA 2022
  Abstract
  Applying Model-Driven Engineering can improve development efficiency. But gaining such benefits for legacy software requires models, and creating them manually is both laborious and error prone. Active automata learning has the potential to make it cost-effective, but practitioners face practical challenges applying it to software components of industrial cyber-physical systems. To overcome these challenges, we present a framework to learn the behavior of component-based software with a client/server architecture, focusing on interfacing isolated component code with an active learning tool. An essential part of the framework is an interfacing protocol that provides a structured way of handling the (a)synchronous communications between the component and learning tool. Our main contribution is the systematic derivation of such interfacing protocols for component-based software, which we demonstrate on the software architecture of ASML, a leading company in developing lithography machines. Through several practical case studies we show that our semi-automatic approach enables setting up a learning environment to learn component behaviors within hours. The protocol’s responsibilities and the way it handles different communication types apply to component-based software in general. Our framework could thus be adapted for companies with similar software architectures.

• Yang, N., Aslam, K., Schiffelers, R., Lensink, L., Hendriks, D., Cleophas, L. and Serebrenik, A. “Improving model inference in industry by combining active and passive learning.” 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER 2019).
  Abstract
  Inferring behavioral models (e.g., state machines) of software systems is an important element of re-engineering activities. Model inference techniques can be categorized as active or passive learning, constructing models by (dynamically) interacting with systems or (statically) analyzing traces, respectively. Application of those techniques in the industry is, however, hindered by the trade-off between learning time and completeness achieved (active learning) or by incomplete input logs (passive learning). We investigate the learning time/completeness achieved trade-off of active learning with a pilot study at ASML, provider of lithography systems for the semiconductor industry. To resolve the trade-off we advocate extending active learning with execution logs and passive learning results. We apply the extended approach to eighteen components used in ASML TWINSCAN lithography machines. Compared to traditional active learning, our approach significantly reduces the active learning time. Moreover, it is capable of learning the behavior missed by the traditional active learning approach.
  
  

Workshops

• Voogd, S.N., Aslam, K., van Gool, L., Theelen, B. and Malavolta, I. “Real-Time
  Collaborative Modeling across Language Workbenches–a Case on Jetbrains MPS and
  Eclipse Spoofax“. HoWCom 2021, 1st hands-on workshop on Collaborative Modeling,
  co-located with MODELS 2021.
  Abstract
  Software tools known as language workbenches are used to define and deploy custom (domain-specific) languages for the purpose of modeling (specific parts) of a system of interest. Because system modeling is a practice that stands to benefit from real-time collaboration, technologies offering real-time collaborative mechanisms for language workbenches are starting to make an appearance. However, these collaboration technologies are generally limited to providing collaboration among clients of a single designated workbench. If the collaborating engineers wish to use different workbenches to work on the model, cross-platform support for collaborative modeling becomes a necessity. In this paper we propose Parsafix, a tool-based approach for achieving real-time collaboration between different language workbenches for users collaborating on models conforming to the same domain-specific language. We propose the main components and mechanisms that make up Parsafix, as well as the implementation of a prototype tool supporting those mechanisms. The prototype tool allows for collaboration between users of JetBrains MPS and Spoofax (within the Eclipse IDE), by making use of the IDEs’ respective real-time collaboration technologies Modelix and Saros. A hands-on session is proposed to showcase the feasibility of having collaborative modeling across different language workbenches through Parsafix.

• Aslam, K., Luo, Y., Schiffelers, R. and van den Brand, M. “Interface protocol inference to aid understanding legacy software components.” 5th International Workshop on Interplay of Model-Driven and Component-Based Software Engineering (ModComp), co-located with MODELS 2018.
  Abstract
  More and more high tech companies are struggling with the maintenance of legacy software. Legacy software is vital to many organizations, so even if its behavior is not completely understood it cannot be thrown away. To re-factor or re-engineer the legacy software components, the external behavior needs to be preserved after replacement so that the replaced components possess the same behavior in the system environment as the original components. Therefore, it is necessary to first completely understand the behavior of components over the interfaces, i.e., the interface protocols, and preserve this behavior during the software modification activities. For this purpose, we present an approach to infer the interface protocols of software components, from the behavioral models of those components learned with a blackbox technique, called active automata learning. We then perform a formal comparison between learned models and reference models ensuring the behavioral relations are preserved. This provides a validation for the learned results, thus developing confidence in applying the active learning technique to reverse engineer the legacy software components in the future.