Interpretable Code Summarization

Code summarization is a process of creating a readable natural language from programming source codes. Code summarization has become a popular research topic for software maintenance, code generation, and code recovery. Existing code summarization methods follow the encoding/decoding approach and us...

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Vydáno v:	IEEE transactions on reliability Ročník 74; číslo 1; s. 2280 - 2289
Hlavní autoři:	Kamal, Md Sarwar, Nimmy, Sonia Farhana, Dey, Nilanjan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.03.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Abstract syntax tree (AST) Algorithms Black boxes code summarization Codes Decoding Encoding-Decoding explainable a Explainable artificial intelligence hidden Markov model (HMM) Hidden Markov models Language LRP Machine learning Mapping Markov chains Natural language Natural languages page rank Recurrent neural networks Source code Source coding Statistical analysis Syntactics Takagi–Sugeno (T–S) fuzzy Words (language)
ISSN:	0018-9529, 1558-1721
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Abstract	Code summarization is a process of creating a readable natural language from programming source codes. Code summarization has become a popular research topic for software maintenance, code generation, and code recovery. Existing code summarization methods follow the encoding/decoding approach and use various machine learning techniques to generate natural language from source codes. Although most of these methods are state of the art, it is difficult to understand the complex encoding and decoding process to map the tokens with natural language words. Therefore, these coding and decoding approaches are treated as opaque models (black box). This research proposes explainable AI methods that overcome the black box features for the token mapping in code summarization process. Here, we created an abstract syntax tree (AST) from the tokens of the source code. We then embedded the AST into natural language words using a bilingual statistical probability approach to generate possible statistical parse trees. We applied a page rank algorithm among the parse trees to rank the trees. From the best-ranked tree, we generate the comment for the corresponding code snippet. To explain our code generation method, we used Takagi-Sugeno fuzzy approach, layerwise relevance propagation and a hidden Markov model. These approaches make our method trustworthy and understandable to humans to understand the process of source code token mapping with natural language words.
AbstractList	Code summarization is a process of creating a readable natural language from programming source codes. Code summarization has become a popular research topic for software maintenance, code generation, and code recovery. Existing code summarization methods follow the encoding/decoding approach and use various machine learning techniques to generate natural language from source codes. Although most of these methods are state of the art, it is difficult to understand the complex encoding and decoding process to map the tokens with natural language words. Therefore, these coding and decoding approaches are treated as opaque models (black box). This research proposes explainable AI methods that overcome the black box features for the token mapping in code summarization process. Here, we created an abstract syntax tree (AST) from the tokens of the source code. We then embedded the AST into natural language words using a bilingual statistical probability approach to generate possible statistical parse trees. We applied a page rank algorithm among the parse trees to rank the trees. From the best-ranked tree, we generate the comment for the corresponding code snippet. To explain our code generation method, we used Takagi–Sugeno fuzzy approach, layerwise relevance propagation and a hidden Markov model. These approaches make our method trustworthy and understandable to humans to understand the process of source code token mapping with natural language words.
Author	Kamal, Md Sarwar Dey, Nilanjan Nimmy, Sonia Farhana
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SubjectTerms	Abstract syntax tree (AST) Algorithms Black boxes code summarization Codes Decoding Encoding-Decoding explainable a Explainable artificial intelligence hidden Markov model (HMM) Hidden Markov models Language LRP Machine learning Mapping Markov chains Natural language Natural languages page rank Recurrent neural networks Source code Source coding Statistical analysis Syntactics Takagi–Sugeno (T–S) fuzzy Words (language)
Title	Interpretable Code Summarization
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