26,098 papers · page 195 of 1,305
Predicting the performance of highly configurable software systems is the foundation for performance testing and quality assurance. To that end, recent work has been relying on machine/deep learning to model software performance. However, a crucial yet unaddressed challenge is ho…
The goal of semantic code search or text-to-code search is to retrieve a semantically relevant code snippet from an existing code database using a natural language query. When constructing a practical semantic code search system, existing approaches fail to provide an optimal bal…
The use of modern Natural Language Processing (NLP) techniques has shown to be beneficial for software engineering tasks, such as vulnerability detection and type inference. However, training deep NLP models requires significant computational resources. This paper explores techni…
Modern optimizing compilers are among the most complex software systems humans build. One way to identify subtle compiler bugs is fuzzing. Both the quantity and the quality of testcases are crucial to the performance of fuzzing. Traditional testcase-generation methods, such as Cs…
Deep code generation is a topic of deep learning for software engineering (DL4SE), which adopts neural models to generate code for the intended functions. Since end-to-end neural methods lack domain knowledge and software hierarchy awareness, they tend to perform poorly w.r.t pro…
Developers spend a significant amount of time in editing code for a variety of reasons such as bug fixing or adding new features. Designing effective methods to predict code edits has been an active yet challenging area of research due to the diversity of code edits and the diffi…
Offensive security-tests are commonly employed to pro-actively discover potential vulnerabilities. They are performed by specialists, also known as penetration-testers or white-hat hackers. The chronic lack of available white-hat hackers prevents sufficient security test coverage…
The field of software security testing, more specifically penetration testing, requires high levels of expertise and involves many manual testing and analysis steps. This paper explores the potential use of large-language models, such as GPT3.5, to augment penetration testers wit…
Software projects experience the departure of developers due to various reasons. As developers are one of the main sources of knowledge in software projects, their absence will inevitably result in a certain degree of knowledge depletion. Bus Factor (BF) is a metric to evaluate h…
In distributed systems and microservice applications, tracing is a crucial observability signal employed for comprehending their internal states. To mitigate the overhead associated with distributed tracing, most tracing frameworks utilize a uniform sampling strategy, which retai…
Defining test oracles is crucial and central to test development, but manual construction of oracles is expensive. While recent neural-based automated test oracle generation techniques have shown promise, their real-world effectiveness remains a compelling question requiring furt…
Differential throughput estimation, i.e., predicting the performance impact of software changes, is critical when developing applications that rely on accurate timing bounds, such as automotive, avionic, or industrial control systems. However, developers often lack access to the …
The safety and trustworthiness of systems with components that are based on Machine Learning (ML) require an in-depth understanding and analysis of all stages in its Development Lifecycle (MLDL). High-level abstractions of desired functionalities, model behaviour, and data are ca…
In industrial practice, GUI (Graphic User Interface) testing of mobile apps still inevitably relies on huge manual efforts. The major efforts are those on understanding the GUIs, so that testing scripts can be written accordingly. Quality assurance could therefore be very labor-i…
Analyzing reliability of Machine Vision Components (MVC) against scene changes (such as rain or fog) in their operational environment is crucial for safety-critical applications. Safety analysis relies on the availability of precisely specified and, ideally, machine-verifiable re…
Deep learning (DL) applications, built upon a heterogeneous and complex DL stack (e.g., Nvidia GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to software and hardware dependencies across the DL stack. One challenge in dependency management across the entire…
Data-intensive scalable computing has become popular due to the increasing demands of analyzing big data. For example, Apache Spark and Hadoop allow developers to write dataflow-based applications with user-defined functions to process data with custom logic. Testing such applica…
Determining program complexity bounds is a fundamental problem with a variety of applications in software development. In this paper we present a novel approach for computing the asymptotic complexity bounds of non-deterministic recursive programs by solving dynamically inferred …
Mutation testing is a powerful technique for assessing and improving test suite quality that artificially introduces bugs and checks whether the test suites catch them. However, it is also computationally expensive and thus does not scale to large systems and projects. One promis…
Software development life cycle is profoundly influenced by bugs; their introduction, identification, and eventual resolution account for a significant portion of software development cost. This has motivated software engineering researchers and practitioners to propose different…