Automated Software Engineering

, Volume 18, Issue 3–4, pp 293–323 | Cite as

Alattin: mining alternative patterns for defect detection

  • Suresh ThummalapentaEmail author
  • Tao Xie


To improve software quality, static or dynamic defect-detection tools accept programming rules as input and detect their violations in software as defects. As these programming rules are often not well documented in practice, previous work developed various approaches that mine programming rules as frequent patterns from program source code. Then these approaches use static or dynamic defect-detection techniques to detect pattern violations in source code under analysis. However, these existing approaches often produce many false positives due to various factors. To reduce false positives produced by these mining approaches, we develop a novel approach, called Alattin, that includes new mining algorithms and a technique for detecting neglected conditions based on our mining algorithm. Our new mining algorithms mine patterns in four pattern formats: conjunctive, disjunctive, exclusive-disjunctive, and combinations of these patterns. We show the benefits and limitations of these four pattern formats with respect to false positives and false negatives among detected violations by applying those patterns to the problem of detecting neglected conditions.


Alternative patterns Static defect detection Mining software engineering data Code search engine 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Acharya, M., Xie, T., Pei, J., Xu, J.: Mining API patterns as partial orders from source code: from usage scenarios to specifications. In: Proc. ESEC/FSE, pp. 25–34 (2007) Google Scholar
  2. Acharya, M., Xie, T., Xu, J.: Mining interface specifications for generating checkable robustness properties. In: Proc. ISSRE, pp. 311–320 (2006) Google Scholar
  3. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of association rules. In: Advances in Knowledge Discovery and Data Mining, pp. 307–328 (1996) Google Scholar
  4. Ammons, G., Bodik, R., Larus, J.R.: Mining specifications. In: Proc. POPL, pp. 4–16 (2002) Google Scholar
  5. Burdick, D., Calimlim, M., Gehrke, J.: MAFIA: a maximal frequent itemset algorithm for transactional databases. In: Proc. ICDE, pp. 443–452 (2001) Google Scholar
  6. Chang, R.-Y., Podgurski, A., Yang, J.: Finding what’s not there: a new approach to revealing neglected conditions in software. In: Proc. ISSTA, pp. 163–173 (2007) CrossRefGoogle Scholar
  7. Bibliography on mining software engineering data. (2010)
  8. Engler, D., Chen, D.Y., Hallem, S., Chou, A., Chelf, B.: Bugs as deviant behavior: a general approach to inferring errors in systems code. In: Proc. SOSP, pp. 57–72 (2001) Google Scholar
  9. Ernst, M., Cockrell, J., Griswold, W., Notkin, D.: Dynamically discovering likely program invariants to support program evolution. IEEE Trans. Softw. Eng. 27(2), 99–123 (2001) CrossRefGoogle Scholar
  10. Google code search engine. (2006)
  11. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Mateo (2000) Google Scholar
  12. The Koders source code search engine. (2005)
  13. Lethbridge, T., Singer, J., Forward, A.: How software engineers use documentation: the state of the practice. In: IEEE Software, pp. 35–39 (2003) Google Scholar
  14. Li, Z., Zhou, Y.: PR-Miner: Automatically extracting implicit programming rules and detecting violations in large software codes. In: Proc. FSE, pp. 306–315 (2005) Google Scholar
  15. Livshits, V.B., Zimmermann, T.: Dynamine: finding common error patterns by mining software revision histories. In: Proc. ESEC/FSE, pp. 296–305 (2005) CrossRefGoogle Scholar
  16. Nanavati, A.A., Chitrapura, K.P., Joshi, S., Krishnapuram, R.: Mining generalised disjunctive association rules. In: Proc. CIKM, pp. 482–489 (2001) Google Scholar
  17. Nguyen, T.T., Nguyen, H.A., Pham, N.H., Al-Kofahi, J.M., Nguyen, T.N.: Graph-based mining of multiple object usage patterns. In: Proc. ESEC/FSE, pp. 383–392 (2009) CrossRefGoogle Scholar
  18. Ramanathan, M.K., Grama, A., Jagannathan, S.: Path-sensitive inference of function precedence protocols. In: Proc. ICSE, pp. 240–250 (2007) Google Scholar
  19. Shimizu, K., Miura, T.: Disjunctive sequential patterns on single data sequence and its anti-monotonicity. In: Proc. MLDM, pp. 376–383 (2005) Google Scholar
  20. Shoham, S., Yahav, E., Fink, S., Pistoia, M.: Static specification mining using automata-based abstractions. In: Proc. ISSTA, pp. 174–184 (2007) CrossRefGoogle Scholar
  21. Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Proc. EDBT, pp. 3–17 (1996) Google Scholar
  22. Thummalapenta, S., Xie, T.: PARSEWeb: a programmer assistant for reusing open source code on the web. In: Proc. ASE, pp. 204–213 (2007) Google Scholar
  23. Thummalapenta, S., Xie, T.: Alattin: mining alternative patterns for detecting neglected conditions. In: Proc. ASE, pp. 283–294 (2009) Google Scholar
  24. Thummalapenta, S., Xie, T.: Mining exception-handling rules as sequence association rules. In: Proc. ICSE, pp. 496–506 (2009) Google Scholar
  25. Wasylkowski, A., Zeller, A., Lindig, C.: Detecting object usage anomalies. In: Proc. ESEC/FSE, pp. 35–44 (2007) Google Scholar
  26. Weimer, W., Necula, G.: Mining temporal specifications for error detection. In: Proc. TACAS, pp. 461–476 (2005) Google Scholar
  27. Williams, C.C., Hollingsworth, J.K.: Recovering system specific rules from software repositories. In: Proc. MSR, pp. 1–5 (2005) CrossRefGoogle Scholar
  28. Yang, J., Evans, D., Bhardwaj, D., Bhat, T., Das, M.: Perracotta: mining temporal API rules from imperfect traces. In: Proc. ICSE, pp. 282–291 (2006) Google Scholar
  29. Zhao, L., Zaki, M.J., Ramakrishnan, N.: BLOSOM: a framework for mining arbitrary boolean expressions. In: Proc. KDD, pp. 827–832 (2006) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Computer ScienceNorth Carolina State UniversityRaleighUSA
  2. 2.IBM ResearchBangaloreIndia

Personalised recommendations