26,098 papers · page 125 of 1,305
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 …
This paper describes a formal repair framework for performance bugs in loop programs, which are programming errors that slow down program execution. The approach is developed based on the observation that a program with a performance bug is a semantically correct program, but it …
Mobile app development necessitates extracting domain-specific, essential, and innovative features that align with user needs and market trends. Determining which features provide a competitive advantage is a complex task, often managed manually by product managers. This study ad…
Mobile app development necessitates the extraction of domain specific, essential and innovative features, aligning with user needs and market dynamics. Identifying features to provide competitive edge to the app developers, is a non-trivial task that is often performed manually b…
Deep Neural Networks (DNNs) for Autonomous Driving Systems (ADS) are typically trained on real-world images and tested using synthetic images from simulators. This approach results in training and test datasets with dissimilar distributions, which can potentially lead to erroneou…
Software failures inform engineering work, standards, regulations. For example, the Log4J vulnerability brought government and industry attention to evaluating and securing software supply chains. Retrospective failure analysis is thus a valuable line of software engineering rese…
Debugging software failures often demands significant time and effort. Program slicing is a technique that can help developers fast track the debugging process by allowing them to focus only on the code relevant to the failure. However, despite the effectiveness of slicing, these…
Automatic code generation for ML systems has gained popularity with the advent of compiler techniques like Multi-Level Intermediate Representation (Multi-Level IR, or MLIR). State-of-the-art MLIR frontends, including IREE-TF, Torch-MLIR, and ONNX-MLIR, aim to bridge the gap betwe…
Recent breakthroughs in Artificial Intelligence (AI) obfuscate the boundaries between digital, physical, and social spaces, a trend expected to continue in the foreseeable future. Traditionally, software engineering has prioritized technical aspects, focusing on functional correc…
Regression testing is important but costly due to the large number of tests to run over frequent changes. Techniques to speed up regression testing such as regression test selection run fewer tests, but they risk missing to run some key tests that detect true faults.
Fuzz testing, an automated technique that introduces random data inputs to systems, has demonstrated remarkable effectiveness in identifying vulnerabilities. Its scalability and automation have made it a focal point of interest in both academic and industrial settings. However, t…
Bug reports, containing crucial information such as the Observed Behavior (OB), the Expected Behavior (EB), and the Steps to Reproduce (S2R), can help developers localize and fix bugs efficiently. However, due to the increasing complexity of some bugs and the limited experience o…
Probabilistic programming allows developers to focus on the modeling aspect in the Bayesian workflow by abstracting away the posterior inference machinery. In practice, however, programming errors specific to the probabilistic environment are hard to fix without deep knowledge of…
With the accelerating adoption of open source software, and an ever-growing body of case studies of security vulnerabilities being introduced by said open source software (Log4J arbitrary code execution, OpenSSH backdoor by way of XZ-utils, and the Polyfill CDN take over), the ne…
Deep Learning (DL) has emerged as a promising means for vulnerability detection due to its ability to automatically derive features from vulnerable code. Unfortunately, current solutions struggle to focus on vulnerability-related parts of vulnerable functions, and tend to exploit…
GUI models encapsulate the desired visual appearance and interactive behaviors of applications, facilitating various downstream tasks like model-based testing (MBT). Manually constructing high-quality GUI models is not only labor-intensive and costly but also prone to errors, par…
Code generation benchmarks such as HumanEval are widely adopted to evaluate LLMs' capabilities. However, after consolidating the latest 24 benchmarks, we noticed three significant imbalances. First, imbalanced programming language. 95.8% of benchmarks involve Python, while only 5…