Abstract The productivity of software developers is influenced by various factors encompassing technical, organizational, and individual aspects. Among these factors, mental health has emerged as a critical element for sustaining performance and well-being in this context. This work primary focus lies in understanding developers’ perceptions and acceptance of metrics related to mental health and productivity. Firstly, a mapping study was conducted to map the existing knowledge in this area. A total of 178 papers were retrieved using a predefined search string. After applying strict inclusion and exclusion criteria, five secondary studies were selected and 99 factors influencing productivity and/or mental health in the workplace were identified. Secondly, a metrics catalog was developed based on these influencing factors, combining established indicators from literature with new metrics designed to monitor developers’ mental health and productivity. The catalog includes 12 metrics along with their respective measurement formulas. Thirdly, an assessment of this catalog was performed through a survey and structured interviews with industry professionals, gathering insights on the applicability and relevance of the proposed metrics. The survey was completed by 47 software developers, 22 of whom also participated in the interviews. Our results reveal that while developers largely recognized the value of the catalog, resistance emerged when these metrics were positioned as evaluative mechanisms in the workplace.

Modern software development relies on cloud-based collaborative platforms (e.g., GitHub and GitLab). In these platforms, developers often employ a pull-based development approach, proposing changes via pull requests and engaging in communication via asynchronous message exchanges. Since communication is key for software development, studies have linked different types of sentiments embedded in the communication to their effects on software projects, such as bug-inducing commits or the non-acceptance of pull requests. In this context, sentiment analysis tools are paramount to detect the sentiment of developers’ messages and prevent potentially harmful impact. Unfortunately, existing state-of-the-art tools vary in terms of the nature of their data collection and labeling processes. Yet, there is no comprehensive study comparing the performance and generalizability of existing tools utilizing a dataset that was designed and systematically curated to this end, and in this specific context. Therefore, in this study, we design a methodology to assess the effectiveness of existing sentiment analysis tools in the context of pull request discussions. For that, we created a dataset that contains ≈ 1.8K manually labeled messages from 36 software projects. The messages were labeled by 19 experts (neuroscientists and software engineers), using a novel and systematic manual classification process designed to reduce subjectivity. By applying these existing tools to the dataset, we observed that while some tools ]perform acceptably, their performance is far from ideal, especially when classifying negative messages. This is interesting since negative sentiment is often related to a critical or unfavorable opinion. We also observed that some messages have characteristics that can make them harder to classify, causing disagreements between the experts and possible misclassifications by the tools, requiring more attention from researchers. Our contributions include valuable resources to pave the way to develop robust and mature sentiment analysis tools that capture/anticipate potential problems during software development.

Robustness is critical for ensuring that software functions correctly under adverse conditions. Exception-handling mechanisms in programming languages enable developers to deal with these adverse conditions. However, implementing exception-related code can present significant challenges to developers. We investigated exception-related code contributions across Java projects in the Apache ecosystem. We analyzed exception-related pull requests (exception-PRs), which were detected using a validated heuristic. We produced a comprehensive dataset of 988 exception-PRs. We observed no statistically significant differences in complexity metrics between exception-PRs and non-exception-PRs. We also found no significant differences in developers' behavior metrics, indicating consistent engagement regardless of whether the pull request addressed exception-related code. A manual analysis revealed that most exception-PRs focused on system improvements rather than bug fixes, suggesting proactive efforts to enhance software robustness. Moreover, the most frequently addressed aspects of exceptional code in these exception-PRs were: (i) the external representation of adverse situations to end-users (more than 40% of the PRs) and (ii) the implementation of effective error-handling actions (nearly 35% of the PRs) to promote program recoverability. Interestingly, a significant proportion of exception-PRs simultaneously addressed multiple aspects. By understanding the nature and characteristics of exception-PRs, we expect to better support developers in managing erroneous conditions and improving software robustness.