4,951 papers · page 8 of 248
Leveraging deep learning (DL)-based code analysis tools to solve software engineering tasks is becoming increasingly popular. Code models often suffer performance degradation due to various reasons (e.g., code data shifts). Retraining is often required to address these issues, bu…
With the recent advances in AI programming assistants such as GitHub Copilot, programming is not limited to classical programming languages anymore-programming tasks can also be expressed and solved by end-users in natural text. Despite the availability of this new programming mo…
Content Security Policy (CSP) is a leading security mechanism for mitigating content injection attacks such as CrossSite Scripting (XSS). Nevertheless, despite efforts from academia and industry, CSP policies (in short, CSPs) are not widely deployed on websites, and deployed CSPs…
Cooperative software verification divides the task of software verification among several verification tools in order to increase efficiency and effectiveness. The basic approach is to let verifiers work on different parts of a program and at the end join verification results. Wh…
Since its inception, UML, the Unified Modeling Language, has been touted as the way to go when it comes to designing and documenting software systems. While being an integral part of many university software engineering programs, UML has found little consideration among developer…
Comments are widely used in source code. If a comment is consistent with the code snippet it intends to annotate, it would aid code comprehension. Otherwise, Code Comment Inconsistency (CCI) is not only detrimental to the understanding of code, but more importantly, it would nega…
Modern compilers, such as LLVM, are complex. Due to their complexity, manual testing is unlikely to suffice, yet formal verification is difficult to scale. End-to-end fuzzing can be used, but it has difficulties in discovering LLVM backend problems for two reasons. First, fronten…
Energy consumption in software systems is becoming increasingly important, especially in large-scale deployments. However, debugging energy-related issues remains challenging due to the lack of specialized tools. This paper presents an energy debugging methodology for identifying…
Autonomous program improvement typically involves automatically producing bug fixes and feature additions. Such program improvement can be accomplished by a combination of large language model (LLM) and program analysis capabilities, in the form of an LLM agent. Since program rep…
Software developers increasingly rely on AI code generation utilities. To ensure that “good” code is accepted into the code base and “bad” code is rejected, developers must know when to trust an AI suggestion. Understanding how developers build this intuition is crucial to enhanc…
Procrastination, the action of delaying or postponing something, is a well-known phenomenon that is relatable to all. While it has been studied in academic settings, little is known about why software developers procrastinate. How does it affect their work? How can developers man…
Effectively managing and resolving software issues is critical for maintaining and evolving software systems. Development teams often rely on issue trackers and issue reports to track and manage the work needed during issue resolution, ranging from issue reproduction and analysis…
Refinement types, a type-based generalization of Floyd-Hoare logics, are an expressive and modular means of statically ensuring a wide variety of correctness, safety, and security properties of software. However, their expressiveness and modularity means that to use them, a devel…
Formal verification is a promising method for producing reliable software, but the difficulty of manually writing verification proofs severely limits its utility in practice. Recent methods have automated some proof synthesis by guiding a search through the proof space using a th…
Costs for resolving software defects increase exponentially in late stages. Incomplete or ambiguous requirements are one of the biggest sources for defects, since stakeholders might not be able to communicate their needs or fail to share their domain specific knowledge. Combined …
The rise of machine learning (ML) and its integration into software systems has drastically changed development practices. While software engineering traditionally focused on manually created code artifacts with dedicated processes and architectures, ML-enabled systems require ad…
Dockerfile flakiness-unpredictable temporal build failures caused by external dependencies and evolving environments-undermines deployment reliability and increases debugging overhead. Unlike traditional Dockerfile issues, flakiness occurs without modifications to the Dockerfile …
Despite being beneficial for rapid delivery of software, Kubernetes deployments can be susceptible to security attacks, which can cause serious consequences. A systematic characterization of how community-prescribed security configurations, i.e., security configurations that are …
Large language models (LLMs) provide effective solutions in various application scenarios, with the support of retrieval-augmented generation (RAG). However, developers face challenges in integrating LLM and RAG into software systems, due to lacking interface specifications, vari…
Algorithmic fairness of machine learning (ML) models has raised significant concern in the recent years. Many testing, verification, and bias mitigation techniques have been proposed to identify and reduce fairness issues in ML models. The existing methods are model-centric and d…