3,458 papers · page 21 of 173
We present CoVeriTeam GUI, a No-Code web frontend to compose new software-verification workflows from existing analysis techniques. Verification approaches stopped relying on single techniques years ago, and instead combine selections that complement each other well. So far, such…
Large language models (LLMs) have revolutionized code completion tasks. IDE plugins such as MarsCode can generate code recommendations, saving developers significant time and effort. However, current evaluation methods for code completion are limited by their reliance on static c…
As Autonomous driving systems (ADS) have transformed our daily life, safety of ADS is of growing significance. While various testing approaches have emerged to enhance the ADS reliability, a crucial gap remains in understanding the accidents causes. Such post-accident analysis is…
Open source software (OSS) development, as any social movement, is driven by its ideologies, namely OSS ideologies [6]. Understanding OSS ideologies could provide significant insights into open source development, since OSS ideologies determine and influence the dynamics and outc…
Smart contracts, self-executing agreements directly encoded in code, are fundamental to blockchain technology, especially in decentralized finance (DeFi) and Web3. However, the rise of Ponzi schemes in smart contracts poses significant risks, leading to substantial financial loss…
Smart contracts, self-executing programs on the blockchain, facilitate reliable value exchanges without centralized oversight. Despite the recent focus on dynamic analysis of their transaction histories in both industry and academia, no open-source tool currently offers comprehen…
Regression Testing Selection (RTS) reduces the cost of regression testing by only running test cases affected by code changes. Due to the bottleneck of single granularity analyses, the latest RTS techniques tend to analyze with mixed granularities. However, a better synergy of th…
Simulink plays an important role in the industry for modeling and synthesis of embedded systems. Ensuring system stability requires using numerous test cases to validate the functionality and safety of the models. However, as requirements increase, the complexity of the models po…
Data wrangling, the process of preparing raw data for further analysis in computational notebooks, is a crucial yet time-consuming step in data science. Code generation has the potential to automate the data wrangling process to reduce analysts' overhead by translating user inten…
Large Language Models (LLMs) have demonstrated remarkable performance in various application domains, largely due to their self-supervised pre-training on extensive high-quality text datasets. However, despite the importance of constructing such datasets, many leading LLMs lack d…
Workflows are pervasive in software systems where business processes and scientific methods are implemented as workflow models to achieve automated process execution. However, despite the benefit of no/low-code workflow automation, creating workflow models requires in-depth domai…
Neural code translation is the task of converting source code from one programming language to another. One of the main challenges is the scarcity of parallel code data, which hinders the ability of translation models to learn accurate cross-language alignments. In this paper, we…
Providing personalized and timely feedback for student's programming assignments is useful for programming education. Automated program repair (APR) techniques have been used to fix the bugs in programming assignments, where the Large Language Models (LLMs) based approaches have …
Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with Large Language Models (LLMs), like ChatGPT, emerging as a leading AIGC model that produces high-quality responses across various applications, including softw…
Large language models (LLMs) like ChatGPT and Gemini have significantly advanced natural language processing, enabling various applications such as chatbots and automated content generation. However, these models can be exploited by malicious individuals who craft toxic prompts t…
Machine learning (ML) models have become essential components in software systems across several domains, such as autonomous driving, healthcare, and finance. The robustness of these ML models is crucial for maintaining the software systems performance and reliability. A signific…
Consistent, diverse, and quality contributions are essential to the sustainability of the open source community. Therefore, it is important that there is infrastructure for effectively onboarding and retaining diverse newcomers to open source software projects. Most often, open s…
Most of the enterprises running Mainframes applications written in COBOL and PL/I languages are on a journey of modernization of these applications to make them maintainable and amenable to the more modular service based programming paradigms. Such enterprises are looking for too…
Configurability is a common property of software allowing programs to be customized for the user. While configurability is pervasive, it can also lead to faults (or misconfigurations) and make program evolution challenging. Dependencies can be missed, essential code can be left i…
Modern automated program repair (APR) tools are well-tuned at repairing single fault programs (i.e., programs in which only one fault can occur at time). However, real-world software projects typically contain multiple bugs at the same time, which can interact with and mask each …