![Naturalness
• Real software tends to be natural, like speech or natural
language.
• It tends to be highly repetitive and predictable.
Naturalness of Software1
[1] A. Hindle, E. Barr, M. Gabel, Z. Su, and P. Devanbu. On the naturalness of software. In ICSE, pages 837–847,
2012.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
More Related Content
What's hot
What's hot (18)
Similar to Review: On the Naturalness of Buggy Code
Similar to Review: On the Naturalness of Buggy Code (20)
Recently uploaded
Recently uploaded (20)
Review: On the Naturalness of Buggy Code
- 1. On the “Naturalness” of Buggy Code Baishakhi Ray, Vincent Hellendoorn, Saheel Godhane, Zhaopeng Tu, Alberto Bacchelli, Premkumar Devanbu. published in ICSE 2016 Jinhan Kim 2018.2.9
- 2. Naturalness • Real software tends to be natural, like speech or natural language. • It tends to be highly repetitive and predictable. Naturalness of Software1 [1] A. Hindle, E. Barr, M. Gabel, Z. Su, and P. Devanbu. On the naturalness of software. In ICSE, pages 837–847, 2012.
- 3. What does it mean when a code is considered “unnatural”?
- 4. Research Questions • Are buggy lines less “natural” than non-buggy lines? • Are buggy lines less “natural" than bug-fix lines? • Is “naturalness" a good way to direct inspection effort?
- 5. Background
- 6. Language Model • Language model assign a probability to every sequence of words. • Given a code token sequence, 𝑆 = 𝑡1 𝑡2 … 𝑡 𝑁
- 7. ngram Language Model • Using only the preceding n - 1 tokens. • ℎ = 𝑡1 𝑡2 … 𝑡𝑖−1
- 8. $gram Language Model2 • Improving language model by deploying an additional cache- list of ngrams extracted from the local context, to capture the local regularities. [2] Z. Tu, Z. Su, and P. Devanbu. On the localness of software. In SIGSOFT FSE, pages 269–280, 2014.
- 9. Study
- 10. Study Subject
- 11. Phase-1 (during active development) • They chose to analyze each project for the period of one-year which contained the most bug fixes in that project’s history. • Then, extract snapshots at 1-month intervals.
- 12. Data Collection
- 14. Entropy Measurement • $gram • The line and file entropies are computed by averaging over all the tokens belong to a line and all lines corresponding to a file respectively.
- 15. Entropy Measurement • Package, class and method declarations • previously unseen identifiers – higher entropy scores • For-loop statements and catch clauses • being often repetitive – lower entropy scores Abstract-syntax-based line-types and computing a syntax-sensitive entropy score
- 16. Syntax-sensitive Entropy Score • Matching between line and AST node. • Then, compute how much a line’s entropy deviated from the mean entropy of its line-type. • => $gram+type
- 17. Relative bug-proneness • => $gram+wType
- 18. Evaluation
- 19. RQ1: Are buggy lines less “natural" than non-buggy lines?
- 20. Are buggy lines less “natural" than non-buggy lines?
- 21. Bug Duration
- 22. Bug Duration Bugs that stay longer in a repository tend to have lower entropy than the short-lived bugs
- 23. RQ2: Are buggy lines less “natural" than bug-fix lines?
- 24. Are buggy lines less “natural" than bug-fix lines?
- 25. Example 1
- 26. Example 2
- 27. Example 3
- 28. Counterexample
- 29. RQ3: Is “naturalness" a good way to direct inspection effort?
- 30. DP: Defect Prediction • Two classifier • Logistic Regression(LR) • Random Forest(RF) • Process metrics • # of developers • # of file-commit • Code churn • Previous bug history
- 31. SBF: Static Bug Finder • SBF uses syntactic and semantic properties of source code. • For this study, PMD and FindBugs are used. • NBF: Naturalness Bug Finder • AUCEC: Area Under the Cost-Effectiveness Curve
- 34. Result • Buggy lines, on average, have higher entropies, i.e. are “less natural”, than non-buggy lines. • Entropy of the buggy lines drops after bug-fixes with statistical significance. • Entropy can be used to guide bug-finding efforts at both the file- level and the line-level.
- 35. Appendix