Context: Many Fault Localization (FL) approaches are designed to locate a single fault, but multiple faults typically exist in large programs. Multi-fault localization approaches at the statement level usually rely on Spectrum-Based Fault Localization (SBFL) without considering Information Retrieval-Based Fault Localization (IRFL). Objective: In this paper, we propose , a multi-fault localization approach that leverages an evolutionary algorithm to locate the code fragment that may be the cause of failure. The evolutionary algorithm can help to efficiently explore the sheer number of candidate code fragments, whereas the fitness function of the evolutionary algorithm combines Ochiai (SBFL) and LDA (IRFL) to assess the suspiciousness of each candidate code fragment. Method: We evaluate using the Defects4J benchmark, and we compare its results against four baselines: SBFL (Ochiai), IRFL (LDA and Blues), and hybrid (Ochiai and Blues). The comparison involves a statistical analysis to provide quantitative evidence of the impact of the results. Results: The results show that significantly outperforms the baselines by a large effect size. Specifically, our approach outperforms the best performing baseline by 51.7%, 19.6%, 11.5% and 11.5% for hit@1, 3, 5 and 10, respectively. Conclusion: Our work provides a new direction for the multi-fault localization of statements using an evolutionary algorithm and combining Ochiai and LDA as fitness function, which is valuable for the automated program repair community since FL directly impacts the accuracy of software fixes.

Abstract The development and maintenance of video games present unique challenges that differentiate them from Classic Software Engineering (CSE) such as the increased difficulty in locating bugs within video games. This distinction has given rise to Game Software Engineering (GSE), a subfield that intersects software engineering and video games. Our work proposes a novel way for bug localization in video games by evolving simulations via an evolutionary algorithm, which helps to explore the large number of possible simulations. Simulations generate data (i.e., traces) from the behavior of non-player characters (NPCs). NPCs are not controlled by the player and are key components of video games. We hypothesize that such traces can be instrumental in locating bugs. Our approach automatically locates potential buggy model elements from traces. Furthermore, we propose a novel way of applying genetic operations to evolve simulations by selectively combining their components, rather than combining all components as a whole. We evaluate our approach in the commercial video game Kromaia, and the results indicate that evolving simulations using our novel component-specific genetic operations boosts bug localization. Specifically, our approach improved the F-measure for all bug categories over randomly combining all components, the baseline (which focuses on CSE and utilizes bug reports), and Random Search by 7.93%, 27.17%, and 46.34%, respectively. This work opens a new research direction for further exploration in bug localization within GSE and potentially in CSE as well. Moreover, it encourages other researchers to explore alternative genetic operations rather than selecting them by default.

Phylogenetics studies the relationships, in terms of biological history and kinship, of a set of taxa (e.g., species). We argue that in Search-based Software Engineering (SBSE), the individuals of an evolutionary computation-driven population could be considered as taxa for which the leverage of Phylogenetic Inference might be beneficial. In this work, we present our Phylogenetics-aware SBSE approach. Our approach introduces a novel Phylogenetic Operation to promote results which are sufficiently aligned (in terms of lineage) with a certain reference given by the domain expert. Our approach is evaluated in two heterogeneous industrial case studies: Procedural Content Generation from Game Software Engineering, and Feature Location from Software Maintenance. The results are analyzed using quality-of-the-solution and acceptance-by-developers measurements. We performed a statistical analysis to determine whether the impact on the results is significant compared to baselines that do not leverage Phylogenetics. The results show that our approach significantly outperforms two baselines in both case studies. Furthermore, two focus groups confirmed the acceptance of our approach and stressed that solution acceptance may make the difference in industrial environments. Our work has the potential to motivate a new breed of research work on Phylogenetics awareness to produce better results in Software Engineering.

Video game engines are used in most modern video games because they simplify and speed up development. In addition, some of the most popular engines, such as Unreal Engine 5 (UE5), also integrate visual scripting tools. Visual scripting in UE5, through Blueprints, is a model-driven development approach that replaces text code, like C++, with a visual language of interconnected nodes representing functions and data flows, forming a flowchart-like logic diagram. This approach simplifies game development by abstracting complex code into intuitive, visual models, enabling creators to construct and iterate game components without extensive programming knowledge. Although Blueprint models usually decrease the complexity of implementing components, thus accelerating the development, they might lead to less energy-efficient runtime performance than C++. In this work, we evaluate the energy consumption of three relevant video game components (health system management, inputs processing, and collections operations for an inventory), each implemented with Blueprint models and C++. The results show that the energy consumption per frame when using C++ is up to 48% lower than when using Blueprint models. The combination of artistic and technical profiles in video game developments has favoured the adoption of Blueprint models. However, there is a lack of works analyzing the energy consumption. Until this work, there was no evidence that the success of models for developing video games, like the one under study in this work, was accompanied by a cost in energy consumption for certain situations. Given the huge popularity of video games, this cost in energy might reach up to the equivalent of the energy consumption of 28 million European households.

Model-driven engineering (MDE) is a software engineering paradigm based on the systematic use of models. Over the past few years, engineers have significantly increased the use of MDE, which has been reported as a successful paradigm for developing industrial software. Recently, there have also been remarkable advancements in the Artificial Intelligence (AI) domain, with a significant increase in advanced Machine Learning (ML) techniques. The advances in both fields have led to a surge in works that dwell within the intersection of ML and MDE. This work places the focus on systematically reviewing works that leverage ML to solve MDE problems. We have reviewed a total of 9,194 papers, selecting 98 studies for further analysis. The results of our Systematic Literature Review (SLR) bring light to the current state of the art and trends in the field, discussing the drift in the usage of the different available ML techniques along with the remaining research gaps and open challenges. Our SLR has the potential to produce a positive impact in the research community by steering it towards ML techniques that have been successfully applied to solve MDE challenges.

In industry, software projects might span over decades, with many engineers joining or leaving the company over time. In these circumstances, no single engineer has all of the knowledge when maintenance tasks such as Traceability Link Recovery (TLR), Bug Localization (BL), and Feature Location (FL) are performed. Thus, collaboration has the potential to boost the quality of maintenance tasks since the solution advanced by one engineer might be enhanced with contributions from other engineers. However, assembling a team of software engineers to collaborate may not be as intuitive as we might think. In the context of a worldwide industrial supplier of railway solutions, this work evaluates how the quality of TLR, BL, and FL is affected by the criteria for selecting engineers for collaboration. The criteria for collaboration are based on engineers’ profile information to select the set of search queries that are involved in the maintenance task. Collaboration is achieved by applying automatic query reformulation, and the location relies on an evolutionary algorithm. Our work uncovers how software engineers who might be seen as not being relevant in the collaboration can lead to significantly better results. A focus group confirmed the relevance of the findings.

Search-Based Software Engineering (SBSE) offers solutions that efficiently explore large complex problem spaces. To obtain more favorable solutions, human participation in the search process is needed. However, humans cannot handle the same number of solutions as an algorithm. We propose the first hybrid fitness function that combines human effort with human simulations. Human effort refers to human participation for providing evaluations of candidate solutions during the search process, whereas human simulations refer to recreations of a scenario in a specific situation for automatically obtaining the evaluation of candidate solutions. We also propose three variants for the hybrid fitness function that vary in the distribution of human effort in order to study whether the variants influence the performance in terms of solution quality. Specifically, we leverage our hybrid fitness function to locate bugs in software models for the video games of game software engineering. Video games are a fertile domain for these hybrid functions because simulated players are naturally developed as part of the video games (e.g., bots in First-Person Shooters). Our evaluation is at the scale of industrial settings with a commercial video game (Play Station 4 and Steam) and 29 professional video game developers. Hybridizing the fitness function outperforms the results of the best baseline by 33.46% in F-measure. A focus group confirms the acceptance of the hybrid fitness function. Hybridizing the fitness function significantly improves the bug localization process by reducing the amount of tedious manual work and by minimizing the number of bugs that go unnoticed. Furthermore, the variant that obtains the best results is a counter-intuitive result that was under the radar of the interactive SBSE community. These results can help not only video game developers to locate bugs, but they can also inspire SBSE researchers to bring hybrid fitness functions to other software engineering tasks.

Video games have characteristics that differentiate their development and maintenance from classic software development and maintenance. These differences have led to the coining of the term Game Software Engineering to name the emerging subfield that intersects Software Engineering and video games. One of these differences is that video game developers perceive more difficulties than other non-game developers when it comes to locating bugs. Our work proposes a novel way to locate bugs in video games by means of evolving simulations. As the baseline, we have chosen BLiMEA, which targets classic software engineering and uses bug reports and the defect localization principle to locate bugs. We also include Random Search as a sanity check in the evaluation. We evaluate the approaches in a commercial video game (Kromaia). The results for F-measure range from 46.80%. to 70.28% for five types of bugs. Our approach improved the results of the baseline by 20.29% in F-measure. To the best of our knowledge, this is the first approach that is designed specifically for bug localization in video games. A focus group with professional video game developers has confirmed the acceptance of our approach. Our approach opens a new research direction for bug localization for both game software engineering and possibly classic software engineering.

Abstract Increasingly, the model driven engineering (MDE) community is paying more attention to the techniques offered by the machine learning (ML) community. This has led to the application of ML techniques to MDE related tasks in hope of increasing the current benefits of MDE. Nevertheless, there is a lack of empirical experiments that evaluate the benefits that ML brings to MDE. In this work, we evaluate the benefits of empowering model engineers of model‐driven development (MDD) with an ML classifier. To do this, we tackled how to embed the ML classifier as part of the MDD. Then, this was evaluated using two different real industrial cases. Our results show that despite the ML part takes an extra effort, the use of the ML classifier pays off in terms of the quality results, the perceived usefulness, and intention to use.

In Search-Based Software Engineering, more than 100 works have involved the human in the search process to obtain better results. However, the case where the human completely replaces the fitness function remains neglected. There is a good reason for that; no matter how intelligent the human is, humans cannot assess millions of candidate solutions as heuristics do. In this work, we study the influence of using the Human as the Fitness Function (HaFF) on the quality of the results. To do that, we focus on Search-Based Model-Driven Engineering (SBMDE) because inspecting models should require less human effort than inspecting code thanks to the abstraction of models. Therefore, we analyze the impact of HaFF in a real-world industrial case study of feature location in models. Furthermore, we also consider a reformulation operation (replacement) in the evaluation because a recent work reported that this operation significantly reduces the number of iterations required in comparison to the widespread crossover and mutation operations. The combination of HaFF and the reformulation operation (HaFF_R) improves the results of the best baseline by 0.15% in recall and 14.26% in precision. Analyzing the results, we learned how to better leverage HaFF_R, which increased the improvement with regard to the best baseline to 1.15% in recall and 20.05% in precision. HaFF_R significantly improves precision because humans are immune to the main limitations of the baselines: vocabulary mismatch and tacit knowledge. A focus group confirmed the acceptance of HaFF. These results are relevant for SBMDE because feature location is one of the main activities performed during maintenance and evolution. Our results, and what we learned from them, can also motivate and help other researchers to explore the benefits of HaFF. In fact, we provide a guideline that further discusses how to apply HaFF to other software engineering problems.

In the combination of Model-Driven Engineering (MDE) and Search-Based Software Engineering (SBSE), genetic operations are one of the key ingredients. Our work proposes a novel adaptation of automatic query reformulations as genetic operations that leverage the latent semantics of software models (the cornerstone artefact of MDE). We analyze the impact of these reformulation operations in a real-world industrial case study of feature location in models. As baselines, we use: 1) the widespread single-point crossover plus random mutation; and 2) mask crossover plus random mutation, which is the best performer for feature location in models. We also perform a statistical analysis to provide quantitative evidence of the impact of the results and to show that this impact is significant. Our reformulation operations improve the results of the best baseline by 37.73 percent in recall and 14.08 percent in precision. These results are relevant for the task of feature location in models (one of the main activities performed during software maintenance and evolution). Furthermore, given that the only requirement to apply our approach is term availability in models, our work opens a new research direction to improve more tasks in MDE such as bug location or requirements traceability.

Textual description of requirements is a specification technique that is widely used in industry, where time is key for success. How requirements are specified textually greatly depends on human factors. In order to study how requirements processing is affected by the level of detail in textual descriptions, this paper compares enriched textual requirements specifications with non-enriched ones. To do this, we have conducted an experiment in industry with 19 engineers of CAF (Construcciones y Auxiliares de Ferrocarril), which is a supplier of railway solutions. The experiment is a crossover design that analyzes efficiency, effectiveness, and perceived difficulty starting from a written specification of requirements that subjects must process in order to build software models. The results show that effectiveness and efficiency for enriched requirements are better, while non-enriched requirements are slightly more difficult to deal with. Therefore, even though enriched requirements require more time to be specified, the results are more successfully when using them.

[Background] Bug-fixing could be complex in industrial practice since thousands of products share features in their configuration. Despite the importance and complexity of bug-fixing, there is still a lack of empirical data about the difficulties found in industrial Software Product Lines (SPLs). [Aims] This paper aims to evaluate engineers' performance fixing errors and propagating the fixes to other configured products in the context of an industrial SPL. [Method] We designed and conducted an empirical study to collect data with regard to bug-fixing tasks within the context of a Induction Hob SPL in the BSH group, the largest manufacturer of home appliances in Europe. [Results] We found that effectiveness, efficiency and satisfaction got reached good values. Through interviews we also found difficulties related to unused features, cloning features unintentionally, detecting modified features, and propagating the fix when the source of the bug is the interaction between features. [Conclusions] The identified difficulties are relevant to know how to better apply SPLs in industry in the future.

Software maintenance and evolution activities are responsible for the emergence of a great demand of feature location approaches that search relevant code in a large codebase. However, this search is usually performed manually and relies heavily on developers. In this paper, we propose a feature location approach that, instead of searching directly into code from a natural language query as other approaches do, transforms a natural language query to a query that is made up of the requirements that are located as relevant. Furthermore, our approach limits the scope of the code search space by selecting only the code of those products that hold relevant requirements. We evaluate the overall effectiveness of our approach in the industrial domain of train control software. Our results show that our approach improves in 18.1% the results of precision with regard to searching directly into code, which encourages further research in this direction.

New technologies are entering medical practice at an astounding pace. However, these technologies often cause to doctors learn and use difficulties. Then, doctors require assistance of a biomedical engineer. This is currently happening in a local hospital that has new technology to analyze biomechanical protocols in patients. Protocols are used to measure performances and identify changes in human body movements and muscles. Doctors are neither familiar with the concepts nor tools used, so biomedical engineers carry out descriptions of protocols rather than doctors. In this paper, we present the design of a domain-specific language that enables doctors to specify biomechanical protocols by addressing learning barriers (using design patterns). We also make doctors' descriptions compatible with the existing tools, and we also support legacy biomedical descriptions (combining meta-modeling and model transformations).

Model-Driven Development (MDD) uses models to guide the development of software systems. However, models are usually conceived to be used by software professionals, so it is quite difficult for end-users to understand them because they are not familiar with domain-specific languages or modeling tools as software professionals are. This makes the end-users' involvement within MDD approaches a difficult task. In this paper, we present a method that introduces a set of techniques and tools that allow end-users to participate from early modeling stages in cooperation with software professionals within any MDD approach. To validate the application of the method, we have applied it in a non-intrusive way within an existing MDD approach for developing smart homes.