Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to ...

The rapid advancement of large language models (LLMs) has revolutionized artificial intelligence, introducing unprecedented capabilities in natural language processing and multimodal content generation. However, the increasing complexity and scale of ...

TOSEM is ACM’s flagship journal for publishing software engineering research. TOSEM stays true to the foundations of the discipline while meaningfully engaging with the wave of disruptive innovations in the field. In this light, we discuss the plans for ...

Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the application, ...

With the increasing usage, scale, and complexity of Deep Learning (dl) models, their rapidly growing energy consumption has become a critical concern. Promoting green development and energy awareness at different granularities is the need of the hour to ...

Detecting atomicity violations effectively in interrupt-driven programs is difficult due to the asymmetric concurrency interleaving of interrupts. Current approaches face two main challenges: (1) A large number of false positives are generated by ...

This work introduces EffFix, a tool that applies a novel static analysis-driven Automated Program Repair (APR) technique for fixing memory errors. APR tools typically rely on a given test-suite to guide the repair process. Apart from the need to provide ...

A program containing placeholders for unspecified statements or expressions is called an abstract (or schematic) program. Placeholder symbols occur naturally in program transformation rules, as used in refactoring, compilation or optimization. Static cost ...

Studies in empirical software engineering are often most useful if they make causal claims because this allows practitioners to identify how they can purposefully influence (rather than only predict) outcomes of interest. Unfortunately, many non-...

With the increasing prevalence of software incorporating deep neural networks (DNNs), quality assurance for these software systems has become a crucial concern. To this end, various methods have been proposed to repair the misbehavior of DNNs by modifying ...

Cloud systems, typically comprised of various components (e.g., microservices), are susceptible to performance issues, which may cause service-level agreement violations and financial losses. Identifying performance issues is thus of paramount importance ...

Context. Technical debt (TD) prevention allows software practitioners to apply practices to avoid potential TD items in their projects. Aims. To uncover and prioritize, from the point of view of software practitioners, the practices that could be used to ...

Command-line options (e.g., -l, -F, -R for ls) given to a command-line program can significantly alternate the behaviors of the program. Thus, fuzzing not only file input but also program options can improve test coverage and bug detection. In this paper, ...

There has been a recent explosion of research on Large Language Models (LLMs) for software engineering tasks, in particular code generation. However, results from LLMs can be highly unstable; nondeterministically returning very different code for the same ...

Metamorphic testing, thanks to its high failure-detection effectiveness especially in the absence of test oracle, has been widely applied in both the traditional context of software testing and other relevant fields such as fault localization and program ...

Fuzzing is an automated software testing technique used to find software vulnerabilities that works by sending large amounts of inputs to a software system to trigger bad behaviors. In recent years, the open-source software ecosystem has seen a ...

This work presents four novel entropy-based indices for measuring the community evolution of developer social networks (DSNs) in open source software (OSS) projects. The proposed indices offer a quantitative measure of community split, shrink, merge, and ...

Since its introduction in November 2022, ChatGPT has rapidly gained popularity due to its remarkable ability in language understanding and human-like responses. ChatGPT, based on GPT-3.5 architecture, has shown great promise for revolutionizing various ...

In recent years, dynamic languages such as Python have become popular due to their flexibility and productivity. The lack of static typing makes programs face the challenges of fixing type errors, early bug detection, and code understanding. To alleviate ...