软件工程

Unlike traditional automation tools or static LLM-based systems, agents combine decision-making and tool utilization to accomplish complex tasks, showing great potential in software engineering. However, existing studies largely focus on…

With the software industry shifting toward a data-driven culture, online A/B testing is a key tool for evaluating new technologies. However, deploying such experiments requires substantial resources, may negatively impact users, and…

Model hallucination is one of the most critical challenges faced by Large Language Models (LLMs), especially in high-stakes code intelligence tasks. As LLMs become increasingly integrated into software engineering tasks, understanding and…

Dyslexia is a common learning disorder that primarily impairs an individual's reading and writing abilities. In adults, dyslexia can affect both professional and personal lives, often leading to mental challenges and difficulties acquiring…

Responsive websites frequently experience distorted layouts at specific screen sizes, called Responsive Layout Failures (RLFs). Manually repairing these RLFs involves tedious trial-and-error adjustments of HTML elements and CSS properties.…

The advances and availability of technologies involving Generative Artificial Intelligence (AI) are evolving clearly and explicitly, driving immediate changes in various work activities. Software Engineering (SE) is no exception and stands…

The rapid development of Large Language Models (LLMs) has transformed software engineering, showing promise in tasks like code generation, bug detection, and compliance checking. However, current models struggle to detect compliance…

Automating the detection of EU General Data Protection Regulation (GDPR) violations in source code is a critical but underexplored challenge. We introduce \textbf{GDPR-Bench-Android}, the first comprehensive benchmark for evaluating diverse…

Adoption of agile practices has increased in IT workforces. However, there is a lack of comprehensive studies in the African context on employee performance when implementing agile practices. This study addresses this gap by exploring…

Code review (CR) is a crucial practice for ensuring software quality. Various automated review comment generation techniques have been proposed to streamline the labor-intensive process. However, existing approaches heavily rely on a single…

The prevalent engagement with mobile apps underscores the importance of understanding their data practices. Transparency plays a crucial role in this context, ensuring users to be informed and give consent before any data access occurs.…

Context: The software maintenance phase involves many activities such as code refactoring, bug fixing, code review or testing. Program comprehension is key to all these activities, as it demands developers to grasp the knowledge (e.g.,…

Code-documentation inconsistencies are common and undesirable: they can lead to developer misunderstandings and software defects. This paper introduces DocPrism, a multi-language, code-documentation inconsistency detection tool. DocPrism…

``Vibe coding'' -- the practice of developing software through iteratively conversing with a large language model (LLM) -- has exploded in popularity within the last year. However, developers report key limitations including the…

The increasing deployment of Large Language Model (LLM) agents for complex software engineering tasks has created a need to understand their problem-solving behaviours beyond simple success metrics. While these agents demonstrate impressive…

Modern software programs are built on stacks that are often undergoing changes that introduce updates and improvements, but may also break any project that depends upon them. In this paper we explore the use of Large Language Models (LLMs)…

The Dafny verifier provides strong correctness guarantees but often requires numerous manual helper assertions, creating a significant barrier to adoption. We investigate the use of Large Language Models (LLMs) to automatically infer…

With the emergence and rapid evolution of large language models (LLM), automating coding tasks has become an important research topic. Many efforts are underway and literature abounds about the efficacy of models and their ability to…

While Large Language Models (LLMs) excel at tool calling, deploying these capabilities in regulated enterprise environments such as fintech presents unique challenges due to on-premises constraints, regulatory compliance requirements, and…

LLMs have become the mainstream approaches to code generation. Existing LLMs mainly employ autoregressive generation, i.e. generating code token-by-token from left to right. However, the underlying autoregressive generation has two…