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Machine learning is part of the daily life of people and companies worldwide. Unfortunately, bias in machine learning algorithms risks unfairly influencing the decision-making process and reiterating possible discrimination. While the interest of the software engineering community in software fairness is rapidly increasing, there is still a lack of understanding of various aspects connected to fair machine learning engineering, i.e., the software engineering process involved in developing fairness-critical machine learning systems. Questions connected to the practitioners' awareness and maturity about fairness, the skills required to deal with the matter, and the best development phase(s) where fairness should be faced more are just some examples of the knowledge gaps currently open. In this paper, we provide insights into how fairness is perceived and managed in practice, to shed light on the instruments and approaches that practitioners might employ to properly handle fairness. We conducted a survey with 117 professionals who shared their knowledge and experience highlighting the relevance of fairness in practice, and the skills and tools required to handle it. The key results of our study show that fairness is still considered a second-class quality aspect in the development of artificial intelligence systems. The building of specific methods and development environments, other than automated validation tools, might help developers to treat fairness throughout the software lifecycle and revert this trend., Competing Interests: Competing InterestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© The Author(s) 2023.)

Source-to-source code translation automatically translates a program from one programming language to another. The existing research on code translation evaluates the effectiveness of their approaches by using either syntactic similarities (e.g., BLEU score), or test execution results. The former does not consider semantics, the latter considers semantics but falls short on the problem of insufficient data and tests. In this paper, we propose MBTA ( M utation- b ased Code T ranslation A nalysis), a novel application of mutation analysis for code translation assessment. We also introduce MTS ( M utation-based T ranslation S core), a measure to compute the level of trustworthiness of a translator. If a mutant of an input program shows different test execution results from its translated version, the mutant is killed and a translation bug is revealed. Fewer killed mutants indicate better code translation. MBTA is novel in the sense that mutants are compared to their translated counterparts, and not to their original program's translation. We conduct a proof-of-concept case study with 612 Java-Python program pairs and 75,082 mutants on the code translators TransCoder and j2py to evaluate the feasibility of MBTA. The results reveal that TransCoder and j2py fail to translate 70.44% and 70.64% of the mutants, respectively, i.e., more than two-thirds of all mutants are incorrectly translated by these translators. By analysing the MTS results more closely, we were able to reveal translation bugs not captured by the conventional comparison between the original and translated programs., Competing Interests: Conflicts of interestThe authors declared that they have no conflict of interest., (© The Author(s) 2023.)

Decision-making software mainly based on Machine Learning (ML) may contain fairness issues (e.g., providing favourable treatment to certain people rather than others based on sensitive attributes such as gender or race). Various mitigation methods have been proposed to automatically repair fairness issues to achieve fairer ML software and help software engineers to create responsible software. However, existing bias mitigation methods trade accuracy for fairness (i.e., trade a reduction in accuracy for better fairness). In this paper, we present a novel search-based method for repairing ML-based decision making software to simultaneously increase both its fairness and accuracy. As far as we know, this is the first bias mitigation approach based on multi-objective search that aims to repair fairness issues without trading accuracy for binary classification methods. We apply our approach to two widely studied ML models in the software fairness literature (i.e., Logistic Regression and Decision Trees), and compare it with seven publicly available state-of-the-art bias mitigation methods by using three different fairness measurements. The results show that our approach successfully increases both accuracy and fairness for 61% of the cases studied, while the state-of-the-art always decrease accuracy when attempting to reduce bias. With our proposed approach, software engineers that previously were concerned with accuracy losses when considering fairness, are now enabled to improve the fairness of binary classification models without sacrificing accuracy., Competing Interests: Conflicts of interestThe authors declare that they have no conflict of interest., (© The Author(s) 2024.)

As research in automatically detecting bugs grows and produces new techniques, having suitable collections of programs with known bugs becomes crucial to reliably and meaningfully compare the effectiveness of these techniques. Most of the existing approaches rely on benchmarks collecting manually curated real-world bugs, or synthetic bugs seeded into real-world programs. Using real-world programs entails that extending the existing benchmarks or creating new ones remains a complex time-consuming task. In this paper, we propose a complementary approach that automatically generates programs with seeded bugs. Our technique, called HyperPUT, builds C programs from a "seed" bug by incrementally applying program transformations (introducing programming constructs such as conditionals, loops, etc.) until a program of the desired size is generated. In our experimental evaluation, we demonstrate how HyperPUT can generate buggy programs that can challenge in different ways the capabilities of modern bug-finding tools, and some of whose characteristics are comparable to those of bugs in existing benchmarks. These results suggest that HyperPUT can be a useful tool to support further research in bug-finding techniques-in particular their empirical evaluation., Competing Interests: Competing interestThe authors declare that they have no known competing interests that could have appeared to influence the work reported in this paper., (© The Author(s) 2024.)

Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of these detectors is still limited and dependent on tunable thresholds. We design and experiment with a novel test smell detection approach based on machine learning to detect four test smells. First, we develop the largest dataset of manually-validated test smells to enable experimentation. Afterward, we train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we compare the ML-based approach with state-of-the-art heuristic-based techniques. The key findings of the study report a negative result. The performance of the machine learning-based detector is significantly better than heuristic-based techniques, but none of the learners able to overcome an average F-Measure of 51%. We further elaborate and discuss the reasons behind this negative result through a qualitative investigation into the current issues and challenges that prevent the appropriate detection of test smells, which allowed us to catalog the next steps that the research community may pursue to improve test smell detection techniques., Competing Interests: Competing interestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© The Author(s) 2024.)

This commentary discusses how papers from the Special Issue fill important gaps in the measurement and quantification of dynamic processes of child behaviors and parent-child interactions linked to child externalizing symptoms. After highlighting some of the innovative qualities of selected papers, challenges and future directions for the development of intensive measurement and dynamic quantitative methods are described. These topics follow from a developmental psychopathology framework that emphasizes measurement using both micro and macro methods, longitudinal research designs, and the recruitment of children that demonstrate clinically meaningful levels of externalizing problem behavior., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)