Characterizing and Classifying Developer Forum Posts Based on Their Intentions
The core message of this article is to propose a taxonomy of intentions for technical forum posts and develop an automated intention detection framework that leverages textual and structural features of posts to accurately classify their underlying purposes.
Challenges in Anonymizing Source Code: Limitations of Existing Techniques
Anonymizing source code to protect developer identities is a challenging problem that cannot be solved through universal k-anonymity, as it is an incomputable task. A relaxed concept of k-uncertainty provides a practical way to measure the level of anonymity, but existing techniques like code normalization, coding style imitation, and code obfuscation fail to provide reliable protection when the attacker is aware of the anonymization.
Research Artifacts in Software Engineering Publications: Current Status and Trends
The software engineering community has seen a significant increase in the prevalence of open-source research artifacts, but the current status and trends of these artifacts remain unclear, warranting further investigation to improve their quality and maintenance.
Optimal Test Sequence Generation for Software Testing using Multi-Objective Particle Swarm Optimization
The core message of this paper is to present a novel approach that employs Multi-Objective Particle Swarm Optimization (MOPSO) to efficiently generate optimal and non-redundant test sequences for comprehensive software testing.
Evaluating Representation Models for Analyzing Stack Overflow Posts
The performance of solutions for analyzing Stack Overflow content hinges significantly on the selection of representation models for Stack Overflow posts. This study comprehensively evaluates the effectiveness of various representation models, including Stack Overflow-specific and general/domain-specific transformer-based models, and proposes SOBERT, a model that consistently outperforms the others by further pre-training on Stack Overflow data.
Open-Source AI-based Software Engineering Tools: Opportunities and Challenges of Collaborative Development
Federated learning can enable collaborative development and maintenance of open-source AI-based software engineering tools while preserving data privacy and enhancing model performance.
A Retrieval-Augmented Generation (RAG) Method for Efficient Source Code Inquiry Using Long-Context Language Models
A RAG-based method that extracts the call tree and source code of relevant functions from the execution trace of a software product, and appends them to the user's inquiry to enable accurate and context-aware responses from long-context language models.
FuSeBMC-AI: An AI-Powered Hybrid Approach for Efficient Test Generation and Program Verification
FuSeBMC-AI employs machine learning techniques, including support vector machines and neural networks, to predict optimal configurations for a hybrid approach combining fuzzing and bounded model checking, leading to improved code coverage and reduced resource consumption.
Navigating Conflict Resolution as a Software Engineer: Strategies for Fostering Productive Teamwork
Effective conflict resolution is crucial for software engineers to maintain productivity, professional relationships, and personal well-being when working in collaborative environments.
Statik: A Decentralized Version Control System Built on IPFS
Statik is a decentralized version control tool that leverages the capabilities of IPFS to provide a secure, efficient, and transparent alternative to traditional centralized version control systems.
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