Database security has become increasingly critical in modern software development, yet developers continue to face significant challenges in implementing and maintaining secure database environments. To support research in this domain, we present DBSecQA, a comprehensive, curated dataset of 22,568 database security-related questions and discussions collected from Stack Overflow and Information Security Stack Exchange. The dataset includes complete metadata (tags, scores, view counts, timestamps, accepted answers) and sophisticated topic categorization using Latent Dirichlet Allocation (LDA) optimized with genetic algorithms, resulting in 20 distinct security topics grouped into 5 main categories. Our dataset enables multiple research directions, including empirical software engineering studies, machine learning applications, educational research, and security tool development. To demonstrate the dataset’s utility, we present preliminary analyses revealing that encryption and authentication represent 41% of all discussions, with encryption-related topics showing the highest percentage of unanswered questions (62.93%). The dataset, along with all collection and processing scripts, is publicly available under an open license, following FAIR data principles for long-term accessibility and reusability.

We present the first four-dimension gold standard dataset to advance opinion mining focused on the software engineering domain. Through a well-defined sampling and annotation strategy leveraging multiple coders, we construct a corpus of 2,000 Stack Overflow posts labeled with four dimensions/tuples, including sentiments, polar facts, aspects, and named entities. This multidimensional ground truth dataset opens up new research opportunities for opinion mining in domain-adapted NLP tools for software engineering by capturing existing relationships between extracted elements at a more granular level. It also facilitates investigating the effects of sentiments in the developers' social forums.

In this study, we quantitatively analyze 1,756 AI-written Python code snippets in the DevGPT dataset and evaluate them for quality and security issues. We systematically distinguish the code snippets as either generated by ChatGPT from scratch (ChatGPT-generated) or modified user-provided code (ChatGPT-modified). The results reveal that ChatGPT-modified code more frequently displays quality issues compared to ChatGPT-generated code. The findings provide insights into the inherent limitations of AI-written code and emphasize the need for scrutiny before integrating such pieces of code into software systems.

Large language models (LLMs) like ChatGPT can generate text for various prompts. With exceptional reasoning capabilities, ChatGPT (particularly the GPT-4 model) has achieved widespread adoption across many tasks - from creative writing to domain-specific inquiries, code generation, and more. This research analyzed the DevGPT dataset to determine common topics posed by developers interacting with ChatGPT. The DevGPT dataset comprises ChatGPT interactions from GitHub issues, pull requests and discussions. By employing a mixed-methods approach combining unsupervised semantic modeling and expert qualitative analysis we categorize the topics developers discuss when interacting with ChatGPT.

This paper presents a large quantitative study of the contributions of females compared to males in open-source projects. Female participation is found substantially low and females are found more engaged in non-coding work compared to men. The findings are statistically significant and are derived from an in-depth analysis of over 10 thousand developers’ contributions to more than 81 million different projects in the World of Code (WoC) infrastructure. The insights from this study are useful in addressing gender disparity in the field.

Automated sentiment analysis in software engineering textual artifacts has long been suffering from inaccuracies in those few tools available for the purpose. We conduct an in-depth qualitative study to identify the difficulties responsible for such low accuracy. Majority of the exposed difficulties are then carefully addressed in developing SentiStrength-SE, a tool for improved sentiment analysis especially designed for application in the software engineering domain. Using a benchmark dataset consisting of 5,600 manually annotated JIRA issue comments, we carry out both quantitative and qualitative evaluations of our tool. SentiStrength-SE achieves 73.85% precision and 85% recall, which are significantly higher than a state-of-the-art sentiment analysis tool we compare with.

Continuous integration is prevalently used in modern software engineering to build software systems automatically. Broken builds hinder developers' work and delay project progress. We must identify the factors causing build failures. This paper presents a large empirical study to identify the factors such as, complexity of a task, build strategy and contribution models (i.e., push and pull request), and projects level attributes (i.e., sizes of projects and teams), which potentially have impacts on the build results. We have studied 3.6 million builds over 1,090 open-source projects. The derived results add to our understanding of the role of those factors on build results, which can be used in minimizing build failures.