On the Investigation of Exception Pull Request Characteristics: Exploring the Apache Ecosystem
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.
Authors
- Marco Aurélio Gerosa (ORCID: https://orcid.org/0000-0003-1399-7535)
- Alessandro Garcia (ORCID: https://orcid.org/0000-0001-5788-5215)
- João Correia (ORCID: https://orcid.org/0000-0001-5190-4825)
- Rafael de Mello (ORCID: https://orcid.org/0000-0002-9877-3946)
- Wesley K. G. Assunção (ORCID: https://orcid.org/0000-0002-7557-9091)
- Jairo Francisco de Souza (ORCID: https://orcid.org/0000-0002-0911-7980)
- Igor Steinmacher (ORCID: https://orcid.org/0000-0002-0612-5790)
- Juliana Alves Pereira (ORCID: https://orcid.org/0000-0002-0799-2829)
- Daniel Coutinho (ORCID: https://orcid.org/0000-0003-4226-2458)
- Alana Carmo de Oliveira (ORCID: https://orcid.org/0000-0002-2530-8242)
- Johny Arriel (ORCID: https://orcid.org/0009-0009-1077-9090)
- Caio Barbosa
Institutions
- Universidade Federal do Rio de Janeiro (BR)
- North Carolina State University (US)
- Northern Arizona University (US)
- Universidade Federal de Pernambuco (BR)
- Pontifícia Universidade Católica do Rio de Janeiro (BR)
Publication Details
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1109/scam63643.2024.00023
- Primary Topic
- Power Systems and Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro