In today’s software development landscape, the extent to which java applications utilize object-oriented programming paradigm remains a subject of interest. Although some researches point to the considerable overhead...
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Code smells, which are suboptimal coding practices that can potentially lead to defects or maintenance issues, can negatively impact the quality of software systems. Most existing code smell detection methods rely on ...
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Code smells, which are suboptimal coding practices that can potentially lead to defects or maintenance issues, can negatively impact the quality of software systems. Most existing code smell detection methods rely on heuristics-based or machine learning (ML) and deep learning (DL)-based techniques. However, these techniques have several drawbacks (e.g., unsatisfactory performance). Large language Models (LLMs) have garnered significant attention in the software engineering (SE) field, achieving state-of-the-art performance across a wide range of SE tasks. Parameter-Efficient Fine-Tuning (PEFT) methods, which are commonly used to adapt LLMs to specific tasks with fewer parameters and reduced computational resources, have emerged as a promising approach for enhancing the performance of LLMs in various SE tasks. However, LLMs have not yet been explored for code smell detection, and their effectiveness for this task remains unclear. Furthermore, no comprehensive investigation has been conducted on the efficiency of PEFT methods for method-level code smell detection. In this regard, we systematically evaluate the effectiveness of state-of-the-art PEFT methods on both small and large language Models (LMs) for method-level code smell detection. To begin, we constructed high-quality java code smell datasets sourced from GitHub. We then fine-tuned four small LMs and six LLMs using various PEFT techniques, including prompt tuning, prefix tuning, LoRA, and (IA)3, for code smell detection. Our comparison against full fine-tuning revealed that PEFT methods not only achieve comparable or better effectiveness but also consume less peak GPU memory. Our analysis further explored the performance of small LMs versus LLMs in the context of code smell detection. Surprisingly, we found that LLMs did not outperform small LMs in this specific task, suggesting that smaller models may be more suited for method-level code smell detection. We also investigated the impact of varying hyper-param
In this paper, we introduce TestBench, a benchmark for class-level LLM-based test case generation. We construct a dataset of 108 java programs from 9 real-world, large-scale projects on GitHub, each representing a dif...
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Software is prone to security vulnerabilities. Program analysis tools to detect them have limited effectiveness in practice due to their reliance on human labeled specifications. Large language models (or LLMs) have s...
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The West java Back-arc (WJBT) in the northern part of the java area was subjected to numerous magnitudes of seismic events in the java Back-arc Thrust system, which are genetically related to the well-known subduction...
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We present a comprehensive dataset of java vulnerability-fixing commits (VFCs) to advance research in java vulnerability analysis. Our dataset, derived from thousands of open-source java projects on GitHub, comprises ...
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The array is a data structure used in a wide range of programs. Its compact storage and constant time random access makes it highly efficient, but arbitrary indexing complicates the analysis of code containing array a...
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In java, some object attributes are mutable, while others are immutable (with the "final" modifier attached to them). Objects that have at least one mutable attribute may be referred to as "mutable"...
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When done manually by engineers at Amazon and other companies, refactoring legacy code in order to eliminate uses of deprecated APIs is an error-prone and time-consuming process. In this paper, we investigate to which...
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tabulapdf is an R package that utilizes the Tabula java library to import tables from PDF files directly into R. This tool can reduce time and effort in data extraction processes in fields like investigative journalis...
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