Icing on the Cake: Automatic Code Summarization at Ericsson

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Detalles Bibliográficos
Publicado en:arXiv.org (Aug 19, 2024), p. n/a
Autor principal: Sridhara, Giriprasad
Otros Autores: Roychowdhury, Sujoy, Soman, Sumit, Ranjani, H G, Britto, Ricardo
Publicado:
Cornell University Library, arXiv.org
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Acceso en línea:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
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045 0 |b d20240819 
100 1 |a Sridhara, Giriprasad 
245 1 |a Icing on the Cake: Automatic Code Summarization at Ericsson 
260 |b Cornell University Library, arXiv.org  |c Aug 19, 2024 
513 |a Working Paper 
520 3 |a This paper presents our findings on the automatic summarization of Java methods within Ericsson, a global telecommunications company. We evaluate the performance of an approach called Automatic Semantic Augmentation of Prompts (ASAP), which uses a Large Language Model (LLM) to generate leading summary comments for Java methods. ASAP enhances the \(LLM's\) prompt context by integrating static program analysis and information retrieval techniques to identify similar exemplar methods along with their developer-written Javadocs, and serves as the baseline in our study. In contrast, we explore and compare the performance of four simpler approaches that do not require static program analysis, information retrieval, or the presence of exemplars as in the ASAP method. Our methods rely solely on the Java method body as input, making them lightweight and more suitable for rapid deployment in commercial software development environments. We conducted experiments on an Ericsson software project and replicated the study using two widely-used open-source Java projects, Guava and Elasticsearch, to ensure the reliability of our results. Performance was measured across eight metrics that capture various aspects of similarity. Notably, one of our simpler approaches performed as well as or better than the ASAP method on both the Ericsson project and the open-source projects. Additionally, we performed an ablation study to examine the impact of method names on Javadoc summary generation across our four proposed approaches and the ASAP method. By masking the method names and observing the generated summaries, we found that our approaches were statistically significantly less influenced by the absence of method names compared to the baseline. This suggests that our methods are more robust to variations in method names and may derive summaries more comprehensively from the method body than the ASAP approach. 
653 |a Performance evaluation 
653 |a Large language models 
653 |a Summaries 
653 |a Information retrieval 
653 |a Ablation 
653 |a Statistical methods 
653 |a Program verification (computers) 
653 |a Methods 
653 |a Open source software 
653 |a Software development 
653 |a Software development tools 
700 1 |a Roychowdhury, Sujoy 
700 1 |a Soman, Sumit 
700 1 |a Ranjani, H G 
700 1 |a Britto, Ricardo 
773 0 |t arXiv.org  |g (Aug 19, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3094928986/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2408.09735