Abstract Traceability is the capability to represent, understand and analyze the relationships between software artefacts. Traceability is at the core of many software engineering activities. This is a blessing in disguise as traceability research is scattered among various research subfields, which impairs a global view and integration of the different innovations around the recording, identification, evaluation and management of traces. This also limits the adoption of traceability solutions in industry. In this sense, the goal of this paper is to present a characterization of the traceability mechanism as a feature model depicting the shared and variable elements in any traceability proposal. The features in the model are derived from a survey of papers related to traceability published in the literature. We believe this feature model is useful to assess and compare different proposals and provide a common terminology and background. Beyond the feature model, the survey we conducted also help us to identify a number of challenges to be solved in order to move traceability forward, especially in a context where, due to the increasing importance of AI techniques in Software Engineering, traces are more important than ever in order to be able to reproduce and explain AI decisions.

Despite the many advantages, the use of external libraries through their APIs remains difficult because of the usage patterns and constraints that are hidden or not properly documented. Existing work provides different techniques to recover API usage patterns from client programs in order to help developers understand and use those libraries. However, most of these techniques produce basic patterns that generally do not involve temporal properties. In this paper, we discuss the problem of temporal usage patterns recovery and propose a genetic-programming algorithm to solve it. Our evaluation on different APIs shows that the proposed algorithm allows to derive non-trivial temporal usage patterns that are useful and generalizable to new API clients.

Software modelling activities typically involve a tedious and time-consuming effort by specially trained personnel. This lack of automation hampers the adoption of the Model Driven Engineering (MDE) paradigm. Nevertheless, in the recent years, much research work has been dedicated to learn MDE artifacts instead of writing them manually. In this context, mono- and multi-objective Genetic Programming (GP) has proven being an efficient and reliable method to derive automation knowledge by using, as training data, a set of examples representing the expected behavior of an artifact. Generally, the conformance to the training example set is the main objective to lead the search for a solution. Yet, single fitness peak, or local optima deadlock, one of the major drawbacks of GP, remains when adapted to MDE and hinders the results of the learning. We aim at showing in this paper that an improvement in populations’ social diversity carried out during the evolutionary computation will lead to more efficient search, faster convergence, and more generalizable results. We ascertain improvements are due to our changes on the search strategy with an empirical evaluation featuring the case of learning well-formedness rules in MDE with a multi-objective genetic algorithm. The obtained results are striking, and show that semantic diversity allows a rapid convergence toward the near-optimal solutions. Moreover, when the semantic diversity is used as for crowding distance, this convergence is uniform through a hundred of runs.

We propose a novel approach for solving the problem of coevolution between metamodels and OCL constraints. Unlike existing solutions, our approach does not rely on predefined update rules and explicit tracking of high level changes to the metamodel. Rather, we pose it as a multi-objective optimization problem, exploring the space of possible OCL modifications to identify solutions that (a) do not violate the structure of the new version of the metamodel, (b) minimize changes to existing constraints, and (c) minimize loss of information. Finally, we recommend an appropriate subset of solutions to the user. We evaluate our approach on three cases of metamodel and OCL coevolution. The results show that we recommend accurate solutions for updating OCL constraints, even for complex evolution changes.

As a contribution to the adoption of the Model-Driven Engineering (MDE) paradigm, the research community has proposed concrete model transformation solutions for the MDE infrastructure and for domain-specific problems. However, as the adoption increases and with the advent of the new initiatives for the creation of repositories, it is legitimate to question whether proposals for concrete transformation problems can be still considered as research contributions or if they respond to a practical/technical work. In this paper, we report on a systematic mapping study that aims at understanding the trends and characteristics of concrete model transformations published in the past decade. Our study shows that the number of papers with, as main contribution, a concrete transformation solution, is not as high as expected. This number increased to reach a peak in 2010 and is decreasing since then. Our results also include a characterization and an analysis of the published proposals following a rigorous classification scheme.