This research presents a personalized adaptive learning system, LAMPAS. Leveraging Large Language Models and a modular learner profiling mechanism, we developed a web-based system that dynamically adapts educational content based on cognitive factors, such as learning styles, cognitive styles, knowledge levels, and skill levels. We introduce a Generate-Review-Refine pipeline to enhance content relevance and completeness and evaluate our system through prompt engineering experiments and LLM benchmarking.

Code review is an important part of the development of any software project. Recently, many open source projects have begun practicing lightweight and tool-based code review (a.k.a modern code review) to make the process simpler and more efficient. However, those practices still require reviewers, of which there may not be sufficiently many to ensure timely decisions. In this paper, we propose a recommender-based approach to be used by open-source projects to increase the number of reviewers from among the appropriate developers. We first motivate our approach by an exploratory study of nine projects hosted on GitHub and Gerrit. Secondly, we build the recommender system itself, which, given a code change, initially searches for relevant reviewers based on similarities between the reviewing history and the files affected by the change, and then augments this set with developers who have a similar development history as these reviewers but have little or no relevant reviewing experience. To make these recommendations, we rely on collaborative filtering, and more precisely, on matrix factorization. Our evaluation shows that all nine projects could benefit from our system by using it both to get recommendations of previous reviewers and to expand their number from among the appropriate developers.

Current Model-Based Systems Engineering (MBSE) practices to design and implement complex systems require modeling and analysis based on many representations: structure, dynamics, safety, security, etc. This induces a large volume of overlapping heterogeneous artefacts which are subject to frequent changes during the project life cycle. In order to verify and validate systems requirements and ensure that models meet user's needs, MBSE techniques shall rely on consistent traceability management. In this paper, we investigate the benefits of Information Retrieval (IR) techniques and the latest advances in Natural Language Processing (NLP) approaches to suggest stakeholders with candidate semantic links generated from the processing of structured and unstructured contents. We illustrate our approach called ATLaS (Aggregation Trace Links Support) through an application on the design and analysis of a mobility service gathering several industrial partners. We provide an empirical evaluation regarding its limitations as part of an industrial MBSE process. Most importantly, we highlight how our method drastically reduces the false positive links generated compared to current IR techniques. The results obtained suggest a good synergy between the presented approach and MBSE techniques.

The digital transformation has brought us to a new horizon where devices, products and services are connected, interact with each other and generate massive amounts of data.What are the products and services to be offered in this environment?How will they be developed, delivered, maintained and evolved?Whatever the field, software, military, health care, or business, processes are everywhere.Not only are processes used to design, produce, and deliver enterprise products and services but also they are also used to instantiate enterprise/government practices, policies, and regulations.Fundamentally, processes are the means by which an enterprise satisfies customers and creates value, and they provide the building blocks of all information systems.Given their importance to the success and profitability of companies, it is more important than ever to seriously consider the design and improvement of these processes and to make sure that they are free of flaws and inconsistencies.At the same time, recent advances in the hardware domain have paved the way for more complex and critical systems.The Internet of things (IoT) applications and cloud computing infrastructures have introduced an increasing need for distributed system applications and more intelligent system segmentation.Big data solutions offer the potential to learn from vast amounts of information generated by these systems and processes (when collected).This information can be used to satisfy business needs or simply to learn and to improve systems and processes.The ICSSP 2017 conference aimed to explore how these new trends impact and constrain the way processes should be designed whatever the type of process and whatever the application domain.The conference also aimed to inquire how new visions and tools that incorporate cloud computing, big data, IoT, and DevOps could be incorporated into processes used to design next generation systems.To this end, ICSSP 2017 sought to address the following questions:1. What will the next generation of process paradigms look like? 2. How will the needs of all business and system stakeholders that participate in the development and evolution of systems and processes be addressed?and provided a venue for specific areas of interest including • Big data driven process modeling, assessment, and improvement • Mining software/business process repositories (including code, bug trackers, and etc) to improve processes • Dynamic process evolution, variability, and adaptability for next generation of process paradigms • Predictive monitoring and process deviance mining • Empirical evidence of the effectiveness of Agile/Lean practices and approaches in software, hardware, and hybrid systems development and evolution • Process issues in developing adaptive and evolving software systems Furthermore, we continued to explore topics that are core to the area of software and systems process including • Continuous process improvement in diverse areas and context

Building a complex system requires the collaboration of different stakeholders. They work together to model the system keeping in mind the requirements described in specification documents. This complexity induces a large volume of requirements and models, i.e., artefacts that will be subject to frequent changes during the project lifetime. Since the artefacts are correlated with each other's, each change has to be rigorously propagated. Identifying traceability links between system's artefacts is then a critical step to reach this goal. In Information Retrieval domain, many approaches have been already proposed to cope with traceability issues. Their main drawback is they introduce an important amount of false positive links making the traceability links validation phase time consuming and error-prone. In this paper, we propose an approach that identifies traceability links with a reduced amount of false positive links ranging from 20% to 30% while raising the amount of true links identified up to 70%. The approach consists of three main steps: 1) we measure syntactical and semantic similarities between pairs of artefacts by combining the use of four major Information Retrieval techniques; 2) using these similarity measures, we identify the most likely true and false links and we build the so called training data set; 3) this training data set and the four IR techniques are used as input of a predictive model in order to classify between true and false links leading ultimately to a reduced amount of false positives. The output is given in the form of a confidence measure that will help the modeller validating the traceability links. We evaluated our approach using four well-known public case studies. Each one comes with a clear identification of true traceability links which allowed us to compare with the outcome of our approach and validate its effectiveness.

Abstract In the last decades, process verification has been intensively addressed and has become an essential activity to correct and to remove errors before process execution. Typical process verification ecosystems propose to express properties to be verified on the process. A property expresses a desired behavior that must hold or not in the process execution. Processes during their lifespan are continuously adapted for several purposes: enriching, correcting, and refactoring the process. When a process is adapted, the existing properties must naturally be rechecked to ensure that no errors have been introduced, ie, the properties still hold. However, the properties may become outdated and must be coadapted w.r.t. the adapted process before to be rechecked. Otherwise, the verification may raise false alarms or may not detect newly introduced errors. In this paper, we propose a coadaptation approach of properties while considering process adaptation for the different dimensions, namely, control flow, object flow, resources, and timing. We systematically studied process changes in the multiple dimensions to identify those that do impact properties and for which we propose resolution strategies. Our preliminary evaluation shows that our resolutions strategies allow to support users in correctly coadapting impacted properties.

Modeling languages, just as all software artifacts, evolve. This poses the risk that legacy models of a company get lost, when they become incompatible with the new language version. To address this risk, a multitude of approaches for metamodel-model co-evolution were proposed in the last 10 years. However, the high number of solutions makes it difficult for practitioners to choose an appropriate approach. In this paper, we present a survey on 31 approaches to support metamodel-model co-evolution. We introduce a taxonomy of solution techniques and classify the existing approaches. To support researchers, we discuss the state of the art, in order to better identify open issues. Furthermore, we use the results to provide a decision support for practitioners, who aim to adopt solutions from research.

Detecting metamodel atomic changes during evolution is prerequisite for co-evolution of models, constraints, and transformations. They are also essential to detect complex changes over the sequence of atomic ones. However when detecting atomic changes with a difference-based technique, the applied order of the atomic changes is not recovered and some hidden changes are undetected. Thus, the quality of the detected atomic change trace is reduced which could be harmful to both co-evolution and detection of complex changes. This paper proposes to identify potential hidden changes in order to add them to the trace of atomic changes, and also to order the atomic changes with ordering heuristics.

Process verification has become an essential activity to correct and to remove errors before process execution. Typical process verification ecosystems propose to express properties to be verified on the process. When a process is adapted, the existing properties must naturally be re-checked to ensure that no errors have been introduced. However, the properties may become outdated and must be co-adapted w.r.t. the adapted process before to be re-checked. Otherwise, the verification may raise false alarms or may not detect newly introduced errors. In this paper, we propose a co-adaptation approach for control-flow process properties. We systematically studied control-flow process changes to identify those that do impact properties, and for which we propose resolution strategies. Our preliminary evaluation shows that our resolutions strategies allow to support users in correctly co-adapting impacted properties.

International audience

Modeling languages evolve regularly. Companies need to maintain all those models that are used in running projects, which can cause these projects to fall back in their schedules. Since 10 years research addresses this issue with approaches for automating co-evolution. The dominant core of these approaches are model resolution strategies. They define 1) how models have to be changed in reaction to specific metamodel changes, 2) what degree of automation can be reached, and 3) to what extent the user can control the resolution outcome. In this paper, we survey existing co-evolution approaches and analyze model resolution strategies. We present a corpus of more than 200 resolution strategies for 116 types of metamodel changes and discuss degree of automation and choices that users have today.

There is an increasing use of model-driven engineering (MDE) in the industry. Despite the existence of research proposals for MDE-specific processes, the question arises whether and how the processes that are already used within a company can be reused, when MDE is introduced. In this position paper we report on a systematic literature review on the question how standard processes, such as SCRUM or the V-Model XT, can be combined with MDE. We come up with the observation that - although it is in some cases possible to reuse standard processes - the combination with MDE can also result in heavyweight changes to a process. Our goal is to draw attention to two arising research needs: the need to collect systematic knowledge about the influence of MDE on software processes and the need to provide guidance for the tailoring of processes based on the set of used MDE techniques.

SUMMARY Ensuring models’ consistency is a key concern when using a model‐based development approach. Therefore, model inconsistency detection has received significant attention over the last years. To be useful, inconsistency detection has to be sound, efficient, and scalable. Incremental detection is one way to achieve efficiency in the presence of large models. In most of the existing approaches, incrementalization is carried out at the expense of the memory consumption that becomes proportional to the model size and the number of consistency rules. In this paper, we propose a new incremental inconsistency detection approach that only consumes a small and model size‐independent amount of memory. It will therefore scale better to projects using large models and many consistency rules. Copyright © 2012 John Wiley & Sons, Ltd.

The growing complexity of processes whatever their kind (i.e. business, software, medical, military) stimulates the adoption of process execution, analysis and verification techniques. However, such techniques cannot be accurately validated as it is not possible to obtain numerous and realistic process models in order to stress test them. The small set of samples and ``toy'' models publically available in the literature is usually insufficient to conduct serious empirical studies and thus, to validate thoroughly work around process analysis and verification. In this paper, we face this problem by proposing a process model generator using a multi-objective genetic algorithm. The originality of our approach comes from the fact that process models are built through a sequence of high-level operations inspired by the way a process modeler could have actually performed to model a process. A working generator prototype has been implemented and shows that it is possible to quickly generate huge, syntactically sound and user-tailored process models.

SUMMARY One major advantage of executable software process models is that once defined, they can be simulated, checked and validated in short incremental and iterative cycles. This also makes them a powerful asset for important process improvement decisions such as resource allocation, deadlock identification and process management. In this paper, we propose a framework that combines Aspect and Model‐driven Engineering approaches in order to ensure process modeling, simulation and execution. This framework is based on UML4SPM, a UML2.0‐based language for Software Process Modeling and Kermeta, an executable metaprogramming language. Copyright © 2010 John Wiley & Sons, Ltd.

Software development companies have been putting a lot of effort in adopting process models, however two main issues remain. On the one hand, process models are inherently incomplete, since companies can not capture all possible situations in a single model. On the other hand, managers can not force process participants (agents) to strictly follow these models. The effect of both issues is that companies need to be able to handle deviations during process enactment. In order to make sure that process agents follow the process model and that their deviations get detected and handled, they adopt the so-called Process-centered Software Engineering Environments (PSEEs). Unfortunately, the options proposed by these tools, when it comes to handling a deviation, are rather limited to basically ignoring or forbidding it. In the present work, we face this limitation by presenting an approach for detecting, managing and tolerating agent deviations. Besides, in this paper we present the formal specification for this approach in the Linear Temporal Logic (LTL). It has been used as a the basis of our PSEE prototype.

Describing and managing activities, resources, and constraints of software development processes is a challenging goal for many organizations. A first generation of Software Process Modeling Languages (SPMLs) appeared in the 1990s but failed to gain broad industrial support. Recently, however, a second generation of SPMLs has appeared, leveraging the strong industrial interest for modeling languages such as UML. In this paper, we propose a comparison of these UML-based SPMLs. While not exhaustive, this comparison concentrates on SPMLs most representative of the various alternative approaches, ranging from UML-based framework specializations to full-blown executable metamodeling approaches. To support the comparison of these various approaches, we propose a frame gathering a set of requirements for process modeling, such as semantic richness, modularity, executability, conformity to the UML standard, and formality. Beyond discussing the relative merits of these approaches, we also evaluate the overall suitability of these UML-based SPMLs for software process modeling. Finally, we discuss the impact of these approaches on the current state of the practice, and conclude with lessons we have learned in doing this comparison.

Process modeling allows for analysis and improvement of processes that coordinate multiple people and tools working together to carry out a task. Process modeling typically focuses on the normative process, that is, how the collaboration transpires when everything goes as desired. Unfortunately, real-world processes rarely proceed that smoothly. A more complete analysis of a process requires that the process model also include details about what to do when exceptional situations arise. We have found that, in many cases, there are abstract patterns that capture the relationship between exception handling tasks and the normative process. Just as object-oriented design patterns facilitate the development, documentation, and maintenance of object-oriented programs, we believe that process patterns can facilitate the development, documentation, and maintenance of process models. In this paper, we focus on the exception handling patterns that we have observed over many years of process modeling. We describe these patterns using three process modeling notations: UML 2.0 Activity Diagrams, BPMN, and Little-JIL. We present both the abstract structure of the pattern as well as examples of the pattern in use. We also provide some preliminary statistical survey data to support the claim that these patterns are found commonly in actual use and discuss the relative merits of the three notations with respect to their ability to represent these patterns.

One major advantage of executable models is that once constructed, they can be run, checked, validated and improved in short incremental and iterative cycles. In the field of software process modeling, process models have not yet reached the level of precision that would allow their execution. Recently the OMG issued a new revision of its standard for software process modeling, namely SPEM2.0. However, even if executability was defined as a mandatory requirement in the RFP (request for proposal), the adopted specification does not fulfill it. This paper presents a critical analysis on the newly defined standard and addresses its lacks in terms of executability. An approach is proposed in order to extend the standard with a set of concepts and behavioural semantics that would allow SPEM2.0 process models to be checked through a mapping to Petri nets and monitored through a transformation into BPEL.

This paper introduces a framework for classifying and comparing three domains related to the process technology. More particularly, it gives process definitions, characteristics, modeling objectives, process model constituents, process context and scope proper to the software process engineering (SPE), business process management (BPM) and workflow management (WfM) domains. It also tends to clarify the relationship between these three process domains. Relationship, which is still a source of ambiguities for many process managers and company's deciders in their choices of the appropriate process technology that will stratify their process modeling goals.

Software processes and software process modeling approaches were and still are in the heart of enterprise preoccupations. This interest is essentially motivated by the increasing need to build reliable and complex software systems in a short time to market. During the three last decades, a variety of Process Modeling Languages were proposed in order to capture the expertise of enterprises. However no one succeeded in gaining the attention of the industry. Main reasons were because that most of them were either imprecise or not suitable to be executed or both. This paper presents UML4SPM, a UML2.0-based Language for software process modelling. Expressiveness, understandability, precision and executability were our main objectives while designing UML4SPM. Our contribution comes inform of MOF-compliant metamodel, a simple yet expressive graphical notation and diagrams and a precise semantics. In order to validate our work, we evaluate UML4SPM with the well-known ISPW-6 process example, a standard benchmark problem for software process modeling