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.

Context: It is frequently claimed the need for bridging the gap between software engineering research and practice. In this sense, the theory of social representations may be useful to characterize the actual concerns of software developers. It comprises the system of values, behaviors, and practices of communities regarding a particular social object, such as the task of smell identification. Aim: To characterize the social representations of smell identification by software developers. Method: Based on the answers given to a question-naire, we analyzed the associations made by the developers about smell identification, i.e., what immediately comes to their minds when they think about this task. Results: We found that developers strongly associate smell identification with the practice of smell removal and with the incidence of bugs. They also frequently associate the task with the practice of inspection and with the need of having individual skills. Besides, we verified that the current state of the art on smell identification partially address the social representations of the software developers. Conclusion: There is a considerable gap between the research of smell identification and its practice. We propose directions to mitigating this gap.