Machine Learning (ML) technologies have shown great promise in many areas, but when used without proper oversight, they can produce biased results that discriminate against historically underrepresented groups. In recent years, the software engineering research community has contributed to addressing the need for ethical machine learning by proposing a number of fairness-aware practices, e.g., fair data balancing or testing approaches, that may support the management of fairness requirements throughout the software lifecycle. Nonetheless, the actual validity of these practices, in terms of practical application, impact, and effort, from the developers’ perspective has not been investigated yet. This paper addresses this limitation, assessing the developers’ perspective of a set of 28 fairness practices collected from the literature. We perform a survey study involving 155 practitioners who have been working on the development and maintenance of ML-enabled systems, analyzing the answers via statistical and clustering analysis to group fairness-aware practices based on their application frequency, impact on bias mitigation, and effort required for their application. While all the practices are deemed relevant by developers, those applied at the early stages of development appear to be the most impactful. More importantly, the effort required to implement the practices is average and sometimes high, with a subsequent average application. The findings highlight the need for effort-aware automated approaches that ease the application of the available practices, as well as recommendation systems that may suggest when and how to apply fairness-aware practices throughout the software lifecycle.

Early security requirements identification is crucial in software development, facilitating the integration of security measures into IT networks and reducing time and costs throughout software life-cycle. This paper addresses the limitations of existing methods that leverage Natural Language Processing (NLP) and machine learning techniques for detecting security requirements. These methods often fall short in capturing syntactic and semantic relationships, face challenges in adapting across domains, and rely heavily on extensive domain-specific data. In this paper we focus on identifying the most effective approaches for this task, highlighting both domain-specific and domain-independent strategies. Our methodology encompasses two primary streams of investigation. First, we explore shallow machine learning techniques, leveraging word embeddings. We test ensemble methods and grid search within and across domains, evaluating on three industrial datasets. Next, we develop several domain-independent models based on BERT, tailored to better detect security requirements by incorporating data on software weaknesses and vulnerabilities. Our findings reveal that ensemble and grid search methods prove effective in domain-specific and domain-independent experiments, respectively. However, our custom BERT models showcase domain independence and adaptability. Notably, the CweCveCodeBERT model excels in Precision and F1-score, outperforming existing approaches significantly. It improves F1-score by ∼ 3% and Precision by ∼ 14% over the best approach currently in the literature. BERT-based models, especially with specialized pre-training, show promise for automating security requirement detection. This establishes a foundation for software engineering researchers and practitioners to utilize advanced NLP to improve security in early development phases, fostering the adoption of these state-of-the-art methods in real-world scenarios.

Traditionally, energy efficiency research has focused on reducing energy consumption at the hardware level and, more recently, in the design and coding phases of the software development life cycle. However, software testing’s impact on energy consumption did not receive attention from the research community. Specifically, how test code design quality and test smell (e.g., sub-optimal design and bad practices in test code) impact energy consumption has not been investigated yet. This study aims to examine open-source software projects to analyze the association between test smell and its effects on energy consumption in software testing. We conducted a mixed-method empirical analysis from two perspectives; software (data mining in 12 Apache projects) and developers’ views (a survey of 62 software practitioners). Our findings show that: (1) test smell is associated with energy consumption in software testing. Specifically, the smelly part of a test case consumes more energy compared to the non-smelly part. (2) certain test smells are more energy-hungry than others, (3) refactored test cases tend to consume less energy than their smelly counterparts, and (4) most developers (45 % of the survey respondents) lack knowledge about test smells’ impact on energy consumption. Based on the results, we emphasize raising developers awareness regarding the impact of test smells on energy consumption. Additionally we present several observations that can direct future research and developments.