Related papers: EnseSmells: Deep ensemble and programming language…

Software fault prediction model are employed to optimize testing resource allocation by identifying fault-prone classes before testing phases. Several researchers' have validated the use of different classification techniques to develop…

Enterprise Architecture Debt (EA Debt) arises from suboptimal design decisions and misaligned components that can degrade an organization's IT landscape over time. Early indicators, Enterprise Architecture Smells (EA Smells), are currently…

This dissertation presents an evaluation of several language models on software defect datasets. A language Model (LM) "can provide word representation and probability indication of word sequences as the core component of an NLP system."…

Traditionally, energy efficiency research has focused on reducing energy consumption at the hardware level and, more recently, in the design and coding phases of the software development life cycle. However, software testing's impact on…

Bug localization is an important aspect of software maintenance because it can locate modules that should be changed to fix a specific bug. Our previous study showed that the accuracy of the information retrieval (IR)-based bug localization…

Technical debt has become a common metaphor for the accumulation of software design and implementation choices that seek fast initial gains but that are under par and counterproductive in the long run. However, as a metaphor, technical debt…

Programming problems can be solved in a multitude of functionally correct ways, but the quality of these solutions (e.g. readability, maintainability) can vary immensely. When code quality is poor, symptoms emerge in the form of 'code…

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell…

Much of the existing ML research focuses on model performance metrics, leaving limited attention to the long-term sustainability and resource efficiency of ML applications. While high performance is essential, ensuring efficient resource…

Using open-source dependencies is essential in modern software development. However, this practice implies significant trust in third-party code, while there is little support for developers to assess this trust. As a consequence, attacks…

Use case modeling is very popular to represent the functionality of the system to be developed, and it consists of two parts: use case diagram and use case description. Use case descriptions are written in structured natural language (NL),…

Meeting the rise of industry demand to incorporate machine learning (ML) components into software systems requires interdisciplinary teams contributing to a shared code base. To maintain consistency, reduce defects and ensure…

Large language models (LLMs) are being increasingly adopted in the software engineering domain, yet the robustness of their grasp on core software design concepts remains unclear. We conduct an empirical study to systematically evaluate…

Modern software relies on a multitude of automated testing and quality assurance tools to prevent errors, bugs and potential vulnerabilities. This study sets out to provide a head-to-head, quantitative and qualitative evaluation of six…

Large language models (LLMs) are increasingly used to generate software artifacts, such as source code, tests, and trace links. Requirements play a central role in shaping the input prompts that guide LLMs, as they are often used as part of…

This Innovative Practice full paper explores how Large Language Models (LLMs) can enhance the teaching of code refactoring in software engineering courses through real-time, context-aware feedback. Refactoring improves code quality but is…

Performance analysis has always been an afterthought during the application development process, focusing on application correctness first. The learning curve of the existing static and dynamic analysis tools are steep, which requires…

Large language models (LLMs) have achieved remarkable progress in code generation, largely driven by the availability of high-quality code datasets for effective training. To further improve data quality, numerous training data optimization…

Nowadays, most DL frameworks (DLFs) use multilingual programming of Python and C/C++, facilitating the flexibility and performance of the DLF. However, inappropriate interlanguage interaction may introduce design smells involving multiple…

The learning and usage of an API is supported by official documentation. Like source code, API documentation is itself a software product. Several research results show that bad design in API documentation can make the reuse of API features…