Artificial intelligence code generation agents have become transformative tools in modern software development, yet their behavioral patterns remain poorly understood. This paper presents a study analyzing pull request patches from the AIDev dataset to characterize the behavioral signatures of five code generation agents (Claude Code, Copilot, Cursor, Devin, and OpenAI Codex) across the top five programming languages (TypeScript, Python, Go, Java, and C#) in the dataset. We investigate two key research questions: “Do agents edit or rewrite existing code?”, and “How repetitive is each agent’s generated code?” Using token-level similarity metrics (Jaccard, TF-IDF, and fuzzy matching) and repetition analysis (n-gram distributions and Shannon entropy), we characterize edit-rewrite behavior by whether new code closely resembles or substantially differs from existing code. Our results show that Claude Code tends toward lower-similarity changes and higher token diversity, Devin tends toward higher-similarity changes indicative of more incremental modification, and OpenAI Codex exhibits mixed patterns across similarity measures. These behavioral patterns provide insights into how different AI agents approach code generation tasks.

Privacy, security, and accessibility, like ethical concerns in mobile applications (a.k.a. apps), commonly subsumed under non-functional requirements, are generally reported by users through app reviews available in app stores. However, these remain unidentified among other types of reviews, such as user experiences, problem reports, and new feature discussions. Over the past decade, extensive research has focused on extracting valuable information from app reviews, including feature requests and bug reports. However, there remains a lack of a synthesis of research related to app review analysis for exploring users' ethical concerns. This paper presents a comprehensive survey of this research area, covering 37 relevant studies published since 2012, identified from the initial 553 studies using specific inclusion and exclusion criteria. The studies examined vary in review counts, ranging from 500 to 626 million, and include between a single and 1.3 million apps. Our detailed analysis highlights diverse objectives, methodologies, and strategies, along with additional resources such as app privacy policies, which researchers generally utilize to analyze ethical concerns. Our findings also identify persistent barriers to privacy, security, accessibility, transparency, fairness, accountability, and safety, as reported by users in app reviews. Furthermore, we propose a research agenda that focuses on four key areas, including automated extraction and classification of ethical concerns-related app reviews. Our survey outcomes can assist developers and system architects in recognizing and prioritizing non-functional requirements at the initial stages of the development lifecycle, whereas researchers can expand upon this synthesis to create tools for the automated detection of ethical concerns.

Bug reports help software development teams enhance software quality, yet their utility is often compromised by unclear or incomplete information. This issue not only hinders developers’ ability to quickly understand and resolve bugs but also poses significant challenges for various software maintenance prediction systems, such as bug triaging, severity prediction, and bug report summarization. To address this issue, we introduce BugsRepo, a multifaceted dataset derived from Mozilla projects that offers three key components to support a wide range of software maintenance tasks.

Bug reports contain the information developers need to triage and fix software bugs. However, unclear, incomplete, or ambiguous information may lead to delays and excessive manual effort spent on bug triage and resolution. In this paper, we explore whether Instruction fine-tuned Large Language Models (LLMs) can automatically transform casual, unstructured bug reports into high-quality, structured bug reports adhering to a standard template. We evaluate three open-source instruction-tuned LLMs (Qwen 2.5, Mistral, and Llama 3.2) against ChatGPT-4o, measuring performance on metrics such as CTQRS, ROUGE, METEOR, and SBERT. Our experiments show that fine-tuned Qwen 2.5 achieves a CTQRS score of (77%), outperforming both fine-tuned Mistral (71%), Llama 3.2 (63%) and ChatGPT in 3-shot learning (75%). Further analysis reveals that Llama 3.2 shows higher accuracy of detecting missing fields particularly Expected Behavior and Actual Behavior, while Qwen 2.5 demonstrates superior performance in capturing Steps-to-Reproduce, with an F1 score of 76%. Additional testing of the models on other popular projects (e.g., Eclipse, GCC) demonstrates that our approach generalizes well, achieving up to 70% CTQRS in unseen projects’ bug reports. These findings highlight the potential of instruction fine-tuning in automating structured bug report generation, reducing manual effort for developers and streamlining the software maintenance process.

Mobile app reviews are a large-scale data source for software improvements. A key task in this context is effectively extracting requirements from app reviews to analyze the users’ needs and support the software’s evolution. Recent studies show that existing methods fail at this task since app reviews usually contain informal language, grammatical and spelling errors, and a large amount of irrelevant information that might not have direct practical value for developers. To address this, we propose a novel reformulation of requirements extraction as a Named Entity Recognition (NER) task based on the sequence-to-sequence (Seq2seq) generation approach. With this aim, we propose a Seq2seq framework, incorporating a BiLSTM encoder and an LSTM decoder, enhanced with a self-attention mechanism, GloVe embeddings, and a CRF model. We evaluated our framework on two datasets: a manually annotated set of 1,000 reviews (Dataset 1) and a crowdsourced set of 23,816 reviews (Dataset 2). The quantitative evaluation of our framework showed that it outperformed existing state-of-the-art methods with an F1 score of 0.96 on Dataset 2, and achieved comparable performance on Dataset 1 with an F1 score of 0.47.

As mobile applications become increasingly integral to our daily lives, concerns about ethics have grown drastically. Users share their experiences, report bugs, and request new features in application reviews, often highlighting safety, privacy, and accountability concerns. Approaches using machine learning techniques have been used in the past to identify these ethical concerns. However, understanding the underlying reasons behind them and extracting requirements that could address these concerns is crucial for safer software solution development. Thus, we propose a novel approach that leverages a knowledge graph (KG) model to extract software requirements from app reviews, capturing contextual data related to ethical concerns. Our framework consists of three main components: developing an ontology with relevant entities and relations, extracting key entities from app reviews, and creating connections between them. This study analyzes app reviews of the Uber mobile application (a popular taxi/ride app) and presents the preliminary results from the proposed solution. Initial results show that KG can effectively capture contextual data related to software ethical concerns, the underlying reasons behind these concerns, and the corresponding potential requirements.