Supporting program comprehension with source code summarization
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This document discusses research on automatically generating summaries of source code to help with program comprehension. It proposes using techniques like latent semantic indexing to extract important lexical and structural information from code, and generate summaries at different granularity levels like class or method. Experiments on an open source project showed that incorporating structural elements like method names into automatic summaries improved their quality compared to only using term frequencies. Future work could develop better techniques to account for structural information when creating code summaries.
Supporting program comprehension with source code summarization
- 1. Supporting Program Comprehension with Source Code Summarization Sonia Haiduc, Jairo Aponte, Andrian Marcus Presented By: Mohammad Masudur Rahman
- 2. Contents 2 Why Code Summarization? Thesis Statement Research Questions about summary Research Questions about tool Automatic Code Summarization Evaluation Experiments Conducted Pyramid Method Important Findings My Observation & Future Works
- 3. Why Code Summarization? Program comprehension 50% of all maintenance works Two extreme approaches – skim through and read thoroughly Skim through – leads to misunderstanding Read thoroughly – time consuming An intermediate solution – source code entity with comprehensive textual description 3
- 4. Thesis Statement New idea: code summarization to help in program comprehension (PC) Applying TR methods like Latent Semantic Indexing in source code summarization. Combining structural information with retrieved code summary to make it effective for realistic purposes. 4
- 5. Research Questions of Code Summarization Summary should be automatically generated Generate summary to different granularity levels – class, method, packages etc Shorter than the source code Capture and preserve code semantics and structure – text as well as structure from the code Consistent structure – important items at first 5
- 6. Research Questions of Code Summarization Summary should reflect the developer’s understanding about the code Tool should allow user to change summary and will remember user’s choice in future summary Tool should rebuild the summary if the code changes or developer’s provide feedback 6
- 7. Research Questions about Summarizer Tool 7 Which summarization technique works the best for source code? What type of structural info necessary in summary? Will the summary be different for different type of maintenance task? How long it would be? How much will it resemble to actual summary? How do developers generate summary?
- 8. Automatic Code Summarization Generate extractive summary – the most important info extracted from the document 8
- 9. Automatic Code Summarization Two types info extracted – lexical and structural Lexical info – identifiers and comments are extracted Common English and PL keywords are removed Identifiers are split into constituent words and stemming performed. 9
- 10. Automatic Code Summarization Extracted lexical info forms the text corpus of code where TR methods (e.g. LSI) used to get most important n words. Once retrieved, n words are combined with structural info like their class name, method name, package name, parameter name and type etc How to apply structural info to autogenerated summary is an important part 10
- 11. Automatic Code Summarization A method name reflects the description of what it does. If method name ignored by TR, the tool can introduce it automatically Additional info can be added like –user tags 11
- 12. Evaluation 12 Two types – intrinsic and extrinsic Intrinsic – content evaluation, how closely it depicts the document or how close to manually generated summary Metrics- precision, recall, pyramid method Extrinsic – how much utility and usability it has to support SE tasks – concept location, impact analysis, software reuse, traceability links recovery etc
- 13. Experiments Conducted Pyramid method ATunes OS project, 12 methods 6 developers from different demographic locations, undergraduate students, 3 years Java programming experiences Developers provided with a list of terms, they need to choose 5 terms for each method that suits best, 60 minutes total time 13
- 14. Experiments Conducted Corpus containing whole code vocabulary Each method is a different document LSI indexing the corpus against each method terms Cosine measure between corpus and method and corpus words are ranked Top 5 words from corpus are chosen 14
- 15. Pyramid method Pyramid score = (Sum of A’s score / Total score A could make) 15
- 16. Pyramid Score 16
- 17. Important Findings 17 Pyramid score >=.1 and <=.5, marked it encouraging Words chosen by developers – 98.7% in method name, 88.9% in class name and 84.6% in parameter name Automatic summary terms – 20% in method name, 12.9% in class name and 30.7% in parameter name Structural info should be considered properly in automatic summary Comments text not included in summary
- 18. My Observation &Future Works 18 The corpus development technique is not well specified- no specification about redundancy protection LSI focuses on term frequency rather than structural info which produces bad scores. During cosine measurement structural info of term in the method could be considered to get better results There should have some heuristic measurement for structural info.