Interpretation-based Code Summarization

Geng, Mingyang; Wang, Shangwen; Dong, Dezun; Wang, Haotian; Cao, Shaomeng; Zhang, Kechi; Jin, Zhi

doi:10.1109/icpc58990.2023.00026

Cited by 7 publications

(1 citation statement)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geng et al [88] proposed a two-stage method for creating natural language comment texts for code. The approach utilized a model interpretation strategy to refine summaries, enhancing accuracy.…”

Section: Code Comment Generationmentioning

confidence: 99%

AI-Assisted Programming Tasks Using Code Embeddings and Transformers

Kotsiantis,

Verykios,

Tzagarakis

2024

Electronics

View full text Add to dashboard Cite

This review article provides an in-depth analysis of the growing field of AI-assisted programming tasks, specifically focusing on the use of code embeddings and transformers. With the increasing complexity and scale of software development, traditional programming methods are becoming more time-consuming and error-prone. As a result, researchers have turned to the application of artificial intelligence to assist with various programming tasks, including code completion, bug detection, and code summarization. The utilization of artificial intelligence for programming tasks has garnered significant attention in recent times, with numerous approaches adopting code embeddings or transformer technologies as their foundation. While these technologies are popular in this field today, a rigorous discussion, analysis, and comparison of their abilities to cover AI-assisted programming tasks is still lacking. This article discusses the role of code embeddings and transformers in enhancing the performance of AI-assisted programming tasks, highlighting their capabilities, limitations, and future potential in an attempt to outline a future roadmap for these specific technologies.

show abstract

Section: Code Comment Generationmentioning

confidence: 99%

AI-Assisted Programming Tasks Using Code Embeddings and Transformers

Kotsiantis,

Verykios,

Tzagarakis

2024

Electronics

View full text Add to dashboard Cite

show abstract

Semantic similarity loss for neural source code summarization

Su,

McMillan

2024

J Software Evolu Process

View full text Add to dashboard Cite

This paper presents a procedure for and evaluation of using a semantic similarity metric as a loss function for neural source code summarization. Code summarization is the task of writing natural language descriptions of source code. Neural code summarization refers to automated techniques for generating these descriptions using neural networks. Almost all current approaches involve neural networks as either standalone models or as part of a pretrained large language models, for example, GPT, Codex, and LLaMA. Yet almost all also use a categorical cross‐entropy (CCE) loss function for network optimization. Two problems with CCE are that (1) it computes loss over each word prediction one‐at‐a‐time, rather than evaluating a whole sentence, and (2) it requires a perfect prediction, leaving no room for partial credit for synonyms. In this paper, we extend our previous work on semantic similarity metrics to show a procedure for using semantic similarity as a loss function to alleviate this problem, and we evaluate this procedure in several settings in both metrics‐driven and human studies. In essence, we propose to use a semantic similarity metric to calculate loss over the whole output sentence prediction per training batch, rather than just loss for each word. We also propose to combine our loss with CCE for each word, which streamlines the training process compared to baselines. We evaluate our approach over several baselines and report improvement in the vast majority of conditions.

show abstract