26,098 papers · page 148 of 1,305
Assuring the correctness of computing systems is fundamental to our society and economy, and formal verification is a class of techniques approaching this issue with mathematical rigor. Researchers have invented numerous algorithms to automatically prove whether a computational m…
There are many approaches for automated software verification, but they are either imprecise, do not scale well to large systems, or do not sufficiently leverage parallelization. This hinders the integration of software model checking into the development process (continuous inte…
When tuning software configuration for better performance (e.g., latency or throughput), an important issue that many optimizers face is the presence of local optimum traps, compounded by a highly rugged configuration landscape and expensive measurements. To mitigate these issues…
Typestate analysis is a commonly used static technique to identify software vulnerabilities by assessing if a sequence of operations violates temporal safety specifications defined by a finite state automaton. Path sensitive typestate analysis (PSTA) offers a more precise solutio…
Control-based self-adaptive systems (control-SAS) are susceptible to deviations from their pre-identified nominal models. If this model deviation exceeds a threshold, the optimal performance and theoretical guarantees of the control-SAS can be compromised. Existing approaches det…
Pre-trained Large Language Models (LLM) have achieved remarkable successes in several domains. However, code-oriented LLMs are often heavy in computational complexity, and quadratically with the length of the input code sequence. Toward simplifying the input program of an LLM, th…
Recently, large language models (LLM) based generative AI has been gaining momentum for their impressive high-quality performances in multiple domains, particularly after the release of the ChatGPT. Many believe that they have the potential to perform general-purpose problem-solv…
The better the code quality and the less complex the code, the easier it is for software developers to comprehend and evolve it. Yet, how do we best detect quality concerns in the code? Existing measures to assess code quality, such as McCabe’s cyclomatic complexity, are decades …
Software engineering activities such as package migration, fixing error reports from static analysis or testing, and adding type annotations or other specifications to a codebase, involve pervasively editing the entire repository of code. We formulate these activities as reposito…
Software metrics such as coverage or mutation scores have been investigated for the automated quality assessment of test suites. While traditional tools rely on software metrics, the field of self-driving cars (SDCs) has primarily focused on simulation-based test case generation …
Python has emerged as one of the most popular programming languages, extensively utilized in domains such as machine learning, data analysis, and web applications. Python’s dynamic nature and extensive usage make it an attractive candidate for dynamic program analysis. However, u…
Sharing research artifacts (e.g., software, data, protocols) is an immensely important topic for improving transparency, replicability, and reusability in research, and has recently gained more and more traction in software engineering. For instance, recent studies have focused o…
Timing side-channel attacks exploit secret-dependent execution time to fully or partially recover secrets of cryptographic implementations, posing a severe threat to software security. Constant-time programming discipline is an effective software-based countermeasure against timi…
Deep reinforcement learning (DRL) has proven extremely useful in a large variety of application domains. However, even successful DRL-based software can exhibit highly undesirable behavior. This is due to DRL training being based on maximizing a reward function, which typically c…
Deep learning-based code generation (DL-CG) applications have shown great potential for assisting developers in programming with human-competitive accuracy. However, lacking transparency in such applications due to the uninterpretable nature of deep learning models makes the auto…
Smart contract transactions associated with security attacks often exhibit distinct behavioral patterns compared with historical benign transactions before the attacking events. While many runtime monitoring and guarding mechanisms have been proposed to validate invariants and st…
Cybersecurity concerns of Internet of Things (IoT) devices and infrastructure are growing each year. In response, organizations worldwide have published IoT security guidelines to protect their citizens and customers by providing recommendations on the development and operation o…
In recent times, a plethora of Large Code Generation Models (LCGMs) have been proposed, showcasing significant potential in assisting developers with complex programming tasks. Within the surge of LCGM proposals, a critical aspect of code generation research involves effectively …
The optimization of a system’s configuration options is crucial for determining its performance and functionality, particularly in the case of autonomous driving software (ADS) systems because they possess a multitude of such options. Research efforts in the domain of ADS have pr…
Deep learning (DL) is a critical tool for real-world applications, and comprehensive testing of DL models is vital to ensure their quality before deployment. However, recent studies have shown that even subtle deviations in DL operators can result in catastrophic consequences, un…