Related papers: What's DAT? Three Case Studies of Measuring Softwa…
Adaptive reasoning is essential for aligning the computational effort of large language models (LLMs) with the intrinsic difficulty of problems. Current chain-of-thought methods boost reasoning ability but indiscriminately generate long…
Test-time adaptation (TTA) addresses distribution shifts for streaming test data in unsupervised settings. Currently, most TTA methods can only deal with minor shifts and rely heavily on heuristic and empirical studies. To advance TTA under…
Test-driven development (TDD) is a programming technique in which the tests are written prior to the source code. It is proposed that TDD is one of the most fundamental practices enabling the development of software in an agile and…
Automated Static Analysis Tools (ASATs) are part of software development best practices. ASATs are able to warn developers about potential problems in the code. On the one hand, ASATs are based on best practices so there should be a…
Without quantitative data, deciding whether and how to use static analysis in a development workflow is a matter of expert opinion and guesswork rather than an engineering trade-off. Moreover, relevant data collected under real-world…
Software engineering (SE) agents powered by large language models are increasingly adopted in practice, yet they often incur substantial monetary cost. We introduce EET, an experience-driven early termination approach that reduces the cost…
Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. However, existing mainstream TTA methods, predominantly operating at batch level, often exhibit suboptimal performance in complex…
Data-Augmentation (DA) is known to improve performance across tasks and datasets. We propose a method to theoretically analyze the effect of DA and study questions such as: how many augmented samples are needed to correctly estimate the…
Many software systems originate as prototypes or minimum viable products (MVPs), developed with an emphasis on delivery speed and responsiveness to changing requirements rather than long-term code maintainability. While effective for rapid…
Artificial Intelligence has demonstrated its significance in software engineering through notable improvements in productivity, accuracy, collaboration, and learning outcomes. This study examines the impact of generative AI tools,…
Large-scale pre-trained language models have shown remarkable results in diverse NLP applications. Unfortunately, these performance gains have been accompanied by a significant increase in computation time and model size, stressing the need…
AI coding assistants have become prolific in recent years. Through a longitudinal mixed-methods investigation, we examined how professional software engineers perceive the effects of AI coding assistants in regard to task focus, developer…
Technical Debt is a common issue that arises when short-term gains are prioritized over long-term costs, leading to a degradation in the quality of the code. Self-Admitted Technical Debt (SATD) is a specific type of Technical Debt that…
Solving mathematical problems requires advanced reasoning abilities and presents notable challenges for large language models. Previous works usually synthesize data from proprietary models to augment existing datasets, followed by…
Adversarial Training (AT), which is commonly accepted as one of the most effective approaches defending against adversarial examples, can largely harm the standard performance, thus has limited usefulness on industrial-scale production and…
This paper compares the impact of Test-Driven Development (TDD) and Behavior-Driven Development (BDD) on software delivery effectiveness within enterprise environments. Using a qualitative research design, data were collected through…
Generative AI technologies promise to transform the product development lifecycle. This study evaluates the efficiency gains, areas for improvement, and emerging challenges of using GitHub Copilot, an AI-powered coding assistant. We…
Recent In-IDE AI coding assistant tools (ACATs) like GitHub Copilot have significantly impacted developers' coding habits. While some studies have examined their effectiveness, there lacks in-depth investigation into the actual assistance…
Proper incentives are important for motivating developers in open-source communities, which is crucial for maintaining the development of open-source software healthy. To provide such incentives, an accurate and objective developer…
The paper presents a systematic methodology for analyzing software developer productivity by refining contribution rate metrics to distinguish meaningful development efforts from anomalies. Using the Mean-High Model Contribution Rate…