26,098 papers · page 63 of 1,305
Fuzzing is a testing technique that generates a large number of inputs to cause program crashes. As software development accelerates and projects scale, the demand for fuzz testing in software assurance has increased. Performing comprehensive fuzz testing on all functions has bec…
The automation of unit test generation has become a critical task for improving the overall efficiency of software development and testing. Many existing techniques attempt to generate a sufficient number of test cases to achieve high code coverage. However, it has been shown tha…
Binary Code Similarity Detection (BCSD), essential for binary-code related tasks like vulnerability detection, has attracted increasing attention in recent years. However, existing methods frequently fall short of achieving both high precision and recall at scale, and their resul…
User tracking is critical in the mobile ecosystem and relies on device identifiers to build user profiles. Early versions of Android allowed third-party apps to easily access non-resettable identifiers such as serial numbers and IMEI. As privacy concerns grew, Google has tightene…
Model checking has achieved remarkable success in the hardware domain, largely due to the accumulation of intricate optimizations and finely tuned implementation details. As tools evolve, diagnosing performance differences to better understand the interplay of these factors has b…
MaxSAT solvers are increasingly used as back-ends in software engineering tools. Yet their results have lacked automatically checkable certificates of optimality. While SAT solvers emit DRAT proofs of (un)satisfiability, MaxSAT must additionally prove that no lower-cost solution …
Existing research has demonstrated promising results when applying large language models (LLMs) to detect security vulnerabilities in source code. However, these studies have been exclusively evaluated on benchmarks from open-source systems, using publicly known vulnerabilities t…
Log-based anomaly detection is a essential task for ensuring the reliability and performance of software systems. However, the performance of existing anomaly detection methods heavily relies on labeling, while labeling a large volume of logs is highly challenging. To address thi…
Large-language-model assistants are suitable for explaining popular APIs, yet they falter on niche or proprietary libraries because the multi-turn dialogue data needed for fine-tuning are scarce. We present APIDA-Chat, an open-source pipeline that converts symbolic dialogue-act "…
Mispredictions by machine learning components can have severe consequences, especially in safety-critical and mission-critical software systems. Therefore, understanding and debugging these mispredictions is a crucial part of the development process for systems that use machine l…
Prompting LLMs with bug-related context (e.g., error messages, stack traces) improves automated program repair, but many bugs still remain unresolved. In real-world projects, developers often rely on broader repository and project-level context beyond the local code to resolve su…
Genetic Improvement (GI) of software automatically creates alternative software versions that are improved according to certain properties of interests (e.g., running-time). Search-based GI excels at navigating large program spaces, but operates primarily at the syntactic level. …
Modern instruction-tuned large language models (LLMs) have made remarkable progress in code generation. However, these LLMs fine-tuned with standard supervised fine-tuning (SFT) sometimes generate plausible-looking but functionally incorrect code fails to emphasize the error-sens…
Large language models (LLMS) have become an essential tool to support developers using traditional text-based programming languages, but the graphical notation of the block-based Scratch programming environment inhibits the use of LLMs. To overcome this limitation, we propose the…
Although privacy policies serve as the primary mechanism for disclosing data practices under regulations like the General Data Protection Regulation, they frequently contain internal conflicts that undermine transparency and user trust. Existing research has advanced automated pr…
Mobile acceptance testing remains a bottleneck in modern software development, particularly for cross-platform mobile development using frameworks like Flutter. While developers increasingly rely on automated testing tools, creating and maintaining acceptance test artifacts still…
Multiple programming tasks require synchronous collaboration between developers, giving rise to real-time collaborative programming tools that enable simultaneous editing of shared source code. However, most existing tools operate at the text level, propagating every keystroke–in…
Fuzzing is widely used for software robustness testing. However, its application in commercial communication modules remains limited due to several key challenges, including labor-intensive template generation, lack of coverage collection support, limited testing performance, and…
While LLM-based specification generation is gaining traction, existing tools primarily focus on mainstream programming languages like C, Java, and even Solidity, leaving emerging and yet verification-oriented languages like Move underexplored. In this paper, we introduce Msg, an …
In large-scale enterprises, on-call engineers (OCEs) are critical for ensuring service availability and reliability. However, as incidents grow in volume and complexity, traditional manual on-call processes are becoming increasingly inadequate. Recent advances in large language m…