As open source software develops, licenses continue to evolve. The previous method of relying on manual analysis has revealed its limitations—the response speed is difficult to keep up with. Therefore, there is an urgent need to introduce automated analysis capabilities into the field of license compliance. However, most existing SCA tools are limited to coarse-grained extraction of license information and lack fine-grained identification of license terms. Even though some studies have attempted to automate the identification of license terms based on NLP or LLMs, they share common drawbacks: First, because the construction of prior data is time-consuming and laborious, most studies choose to directly use unverified, low-quality data provided by some license platforms. Second, there is currently no research comparing the two different technical routes of NLP-based and LLM-based, resulting in a lack of clear understanding within the field of which technical route is more suitable for automatic identification of licensing terms. To address this gap, this study aims to explore the different performances of license terms identified using different technical routes based on the same quality dataset. Specifically, we first constructs a high-quality term label dataset based on the SPDX license list. Then, based on this dataset, we model the usage scenarios of the terms involved in the licenses to describe the relationships between terms. Finally, based on the dataset and term modeling, a learning model LtRecognizer, which includes named entity label, syntax parsing, attitude judgment, and named entity recognition, is designed. The model is trained on processed label data to achieve automatic recognition and structured extraction of new input license terms and attitudes, with an accuracy rate of up to 93.59
Large language model (LLM)-based automated program repair (APR) has made rapid progress in recent years, but three challenges remain: inaccurate root-cause reasoning, unstable patch generation, and unreliable final patch selection. To address these issues, we propose CGARF, a causality-guided multi-agent repair framework that improves the repair stage of existing LLM-based localization pipelines. CGARF includes three components. First, CG-MAD (Causal Graph-Augmented Multi-Agent Debate) constructs causal chains and re-ranks candidate locations to improve root-cause identification. Second, SRCD (Self-Reflection Consistency Distillation) combines dynamic resource allocation with reflection-guided prompt adjustment to improve the stability and consistency of patch generation. Third, TSPF (Two-Stage Patch Filtering) integrates functional testing, causal matching, and group-similarity selection to improve the reliability of final patch selection. We evaluate CGARF on SWE-bench Lite and SWE-bench Verified with three localization methods (Agentless, Orcaloca, and CoSIL) and four models, including open-source models (Qwen2.5-Coder-32B and Qwen3-Coder-30B) and closed-source models (GPT-4o and GPT-4.1). Across 12 evaluated configurations, CGARF consistently improves repair success rates by 2–4 percentage points and increases patch applicability by about 3 percentage points on average. Ablation results further show that the three components play complementary roles in root-cause reasoning, patch generation stability, and final patch selection. These results suggest that causal-aware reasoning and stability-oriented design can improve the effectiveness of LLM-based APR. In the evaluated settings, CGARF serves as a plug-in repair enhancement framework that improves both repair performance and patch reliability. We have open-sourced CGARF at: https://github.com/thele689/CGARF
Many volunteers contribute learning resources for software development technologies online. We interviewed 26 volunteer contributors, i.e. documentors, to understand why and how they design, create, and contribute software documentation. From a qualitative analysis of our interviews, we identified sixteen considerations that documentors have during the contribution process, along three dimensions, namely motivations, topic selection techniques, and styling objectives. We grouped conceptually related considerations to elicit five mindsets that documentors have during the creation of software documentation. We contribute a framework for describing documentor mindsets, and their associated considerations across the three dimensions. This framework provides a structure and vocabulary for reasoning about the documentation contribution process. Based on the framework, we surface the need to reconsider existing perspectives of the documentation creation process, including the role of the documentor and metrics to measure documentation quality. Our insights can inform software documentation creation tools and future research on user generated learning resources.
Large language models (LLMs) are rapidly reshaping software development, but their impact across the full software development lifecycle is underexplored. Existing work tends to focus on isolated activities such as code generation or testing, leaving open questions about how LLMs affect developers, processes, products, and the broader software ecosystem. We address this gap through semi-structured interviews with sixteen early-adopter software professionals who integrated LLM-based tools into their day-to-day work in early to mid-2023. We treat these interviews as early empirical evidence and compare participants’ accounts with recent work on LLMs in software engineering, noting which early patterns persist or shift. Using thematic analysis, we organize our findings around four dimensions: people, process, product, and society. Developers reported substantial productivity gains from reducing mundane tasks, streamlining search, and accelerating debugging, but also described a productivity-quality paradox: they frequently discarded generated code and shifted effort from writing code to critically evaluating and integrating it. LLM use was highly phase-dependent, with strong uptake in implementation and debugging but limited influence on requirements gathering and collaborative work. Participants developed new competencies to use LLMs effectively, including prompt engineering strategies, multi-layered verification, and security-conscious integration to protect proprietary data. They also anticipated changes in hiring expectations, team practices, and computing education, while emphasizing that human judgment and foundational software engineering skills remain essential. Our findings, consistent with evidence from large-scale studies, offer actionable implications for developers, organizations, educators, and tool designers seeking to integrate LLMs responsibly into professional software practice.
