26,098 papers · page 54 of 1,305
Data-driven software is increasingly being used as a critical component of automated decision-support systems. Since this class of software learns its logic from historical data, it can encode or amplify discriminatory practices. Previous research on algorithmic fairness has focu…
Machine learning (ML) components are increasingly incorporated into software products for end-users, but developers face challenges in transitioning from ML prototypes to products. Academics have limited access to the source of commercial ML products, hindering research progress …
The high number of cyber threats poses significant challenges, with impactful software exploits ranging from data theft to ransomware deployment. Unfortunately, past research highlighted limited security expertise within development teams. Collaboration between developers and sec…
The emergence of Large Language Models (LLMs) has accelerated the progress of intelligent software engineering technologies, which brings promising possibilities for unit test generation. However, existing approaches for unit tests directly generated from Large Language Models (L…
Understanding the work styles of diverse programmers can help build inclusive workplaces, enabling all software engineers to excel. An estimated 10.6 % of programmers have Attention Deficit Hyperactivity Disorder (ADHD), a condition characterized by differences in attention and w…
Code Large Language Models (LLMs) have demonstrated remarkable capabilities in generating, understanding, and manipulating programming code. However, their training process inadvertently leads to the memorization of sensitive information, posing severe privacy risks. Existing stu…
This paper introduces DSrepair, a knowledge-enhanced program repair approach designed to repair the buggy code generated by LLMs in the data science domain. DSrepair uses knowledge graph based RAG for API knowledge retrieval and bug knowledge enrichment to construct repair prompt…
Knowledge distillation compresses large language models (LLMs) into more compact and efficient versions that achieve similar accuracy on code-related tasks. However, as we demonstrate in this study, compressed models are four times less robust than the original LLMs when evaluate…
Recent research has shown that incorporating bug-related facts, such as stack traces and GitHub issues, into prompts enhances the bug-fixing capabilities of large language models (LLMs). Considering the ever-increasing context window of these models, a critical question arises: w…
Large Language Models (LLMs) have been excellent in generating and reasoning about source code and natural-language texts. They can recognize patterns, syntax, and semantics in code, making them effective in several software engineering tasks. However, they exhibit weaknesses in …
Large Language Models (LLMs) have found widespread applications in various domains, including web applications with chatbot interfaces. Aided by an LLM-integration middleware such as LangChain, user prompts are translated into SQL queries used by the LLM to provide meaningful res…
Fully homomorphic encryption (FHE) is a promising cryptographic primitive that enables secure computation over encrypted data. A primary use of FHE is to support privacypreserving machine learning (ML) on public cloud infrastructures. Despite the rapid development of FHE-based ML…
Ensuring usability is crucial for the success of mobile apps. Usability issues can compromise user experience and negatively impact the perceived app quality. This paper presents UX-LLM, a novel tool powered by a Large Vision-Language Model that predicts usability issues in iOS a…
With the advent of data-centric and machine learning (ML) systems, data quality is playing an increasingly critical role for ensuring the overall quality of software systems. Data preparation, an essential step towards high data quality, is known to be a highly effort-intensive p…
Deep Learning (DL) has achieved significant success in socially critical decision-making applications but often exhibits unfair behaviors, raising social concerns. Among these unfair behaviors, individual discrimination-examining inequalities between instance pairs with identical…
One major challenge of regression testing are flaky tests, i.e., tests that may pass in one run but fail in another run for the same version of code. One prominent category of flaky tests is order-dependent (OD) flaky tests, which can pass or fail depending on the order in which …
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…