This article is a summary of a study focusing on Artificial Intelligence (AI) based sourcecode analysis amidst the complexity of software development and rapidly evolving technological needs. The study evaluates anal...
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ISBN:
(数字)9783031700187
ISBN:
(纸本)9783031700170;9783031700187
This article is a summary of a study focusing on Artificial Intelligence (AI) based sourcecode analysis amidst the complexity of software development and rapidly evolving technological needs. The study evaluates analyses conducted to improve code quality, detect errors, and perform code optimization by examining the potential impacts of AI in software development processes. The time spent on research and experiments for detecting and resolving errors in the software development process has been a constant source of concern. In this context, the results of using unoptimized sourcecode often lead to outputs that directly affect complex and maintenance costs. The topic has been extensively addressed in the literature as a comprehensive subject known as AI, code Intelligence (CI), and Programming Language Processing (PLP) and has been the focus of various surveys and application studies. The article suggests that the use of AI could be a potential solution to increase efficiency and minimize errors in software development processes. In the study, two different AI tools, namely ChatGPT and Gemini, were used to address problem resolution. Two different models, GPT4 and Gemini, were included in the analysis process. JavaScript was the preferred language for obtaining sourcecode, which was sourced from the GitHub platform.
JavaScript is one of the most used programming languages for front-end development of Web applications. The increase in complexity of front-end features brings concerns about performance, especially the load and execu...
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JavaScript is one of the most used programming languages for front-end development of Web applications. The increase in complexity of front-end features brings concerns about performance, especially the load and execution time of JavaScript code. In this paper, we propose an evolutionary program improvement technique to reduce the size of JavaScript programs and, therefore, the time required to load and execute them in Web applications. To guide the development of this technique, we performed an experimental study to characterize the patches applied to JavaScript programs to reduce their size while keeping the functionality required to pass all test cases in their test suites. We applied this technique to 19 JavaScript programs varying from 92 to 15,602 LOC and observed reductions from 0.2 to 73.8 percent of the original code, as well as a relationship between the quality of a program's test suite and the ability to reduce the size of its sourcecode.
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