PBCOV: a property-based coverage criterion
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Coverage criteria aim at satisfying test requirements and compute metrics values that quantify the adequacy of test suites at revealing defects in programs. Typically, a test requirement is a structural program element, and the coverage metric value represents the percentage of elements covered by a test suite. Empirical studies show that existing criteria might characterize a test suite as highly adequate, while it does not actually reveal some of the existing defects. In other words, existing structural coverage criteria are not always sensitive to the presence of defects. This paper presents PBCOV, a Property-Based COVerage criterion, and empirically demonstrates its effectiveness. Given a program with properties therein, static analysis techniques, such as model checking, leverage formal properties to find defects. PBCOV is a dynamic analysis technique that also leverages properties and is characterized by the following: (a) It considers the state space of first-order logic properties as the test requirements to be covered; (b) it uses logic synthesis to compute the state space; and (c) it is practical, i.e., computable, because it considers an over-approximation of the reachable state space using a cut-based abstraction.We evaluated PBCOV using programs with test suites comprising passing and failing test cases. First, we computed metrics values for PBCOV and structural coverage using the full test suites. Second, in order to quantify the sensitivity of the metrics to the absence of failing test cases, we computed the values for all considered metrics using only the passing test cases. In most cases, the structural metrics exhibited little or no decrease in their values, while PBCOV showed a considerable decrease. This suggests that PBCOV is more sensitive to the absence of failing test cases, i.e., it is more effective at characterizing test suite adequacy to detect defects, and at revealing deficiencies in test suites.
KeywordsSoftware testing Coverage criteria Property-based coverage State space coverage Specification-based coverage Test suite evaluation Reachability analysis Logic synthesis
- ABC. (2007). ABC: Berkeley logic synthesis and verification group. a system for sequential synthesis and verification, release 70930. http://www.eecs.berkeley.edu/alanmi/abc/.
- Ammann, P., & Black, P. E. (2001). A specification-based coverage metric to evaluate test sets. International Journal of Reliability, Quality and Safety Engineering, 8(4), 239–248.Google Scholar
- Ball, T. (2004). A theory of predicate-complete test coverage and generation. In In FMCO 2004: Symposium on formal methods for components and objects, pp 1–22.Google Scholar
- Ball, T., Majumdar, R., Millstein, T., & Rajamani, SK. (2001). Automatic predicate abstraction of c programs. In Programming language design and implementation, ACM, New York, NY, USA, PLDI ’01, pp. 203–213.Google Scholar
- Barr, A. (2004). Find the bug: A iook of incorrect programs. Reading: Addison-Wesley Professional.Google Scholar
- Baudin, P., Filliâtre, J.C., Hubert, T., Marché, C., Monate, B., Moy, Y., et al. (2009). ACSL: ANSI C specification language (preliminary design V1.9). http://www.frama-c.cea.fr/acsl.html.
- Boyapati, C., Khurshid, S., & Marinov, D. (2002). Korat: Automated testing based on java predicates. In International symposium on software testing and analysis (ISSTA), pp. 123–133.Google Scholar
- Burnim, J., & Sen, K. (2008). Heuristics for scalable dynamic test generation. In International conference on automated software engineering, pp. 443–446.Google Scholar
- Chang, J., & Richardson, DJ. (1999). Structural specification-based testing: Automated support and experimental evaluation. In European, software engineering conference, ESEC/FSE-7, pp. 285–302.Google Scholar
- Clarke, E., Brumberg, J. O., & Peled, D. A. (1999). Model checking. Cambridge: MIT Press.Google Scholar
- Clarke, E., Kroening, D., & Lerda, F. (2004). A tool for checking ansi-c programs. In Tools and algorithms for the construction and analysis of systems, pp. 168–176.Google Scholar
- Dutertre, B., & Moura, L. M. D. (2006). A fast linear-arithmetic solver for dpll(t). Computer Aided Verification.Google Scholar
- Ford, L. R., & Fulkerson, D. R. (1956). Maximal flow through a network. Canadian Journal of Mathematics, 8, 399–404.Google Scholar
- Gligoric, M., Gvero, T., Jagannath, V., Khurshid, S., Kuncak, V., & Marinov, D. (2010). Test generation through programming in udita. In International conference on software engineering, ACM, ICSE ’10.Google Scholar
- Godefroid, P., Klarlund, N., & Sen, K. (2005). Dart: Directed automated random testing. In Proceedings of the 2005 ACM SIGPLAN conference on programming language design and implementation, ACM, PLDI ’05.Google Scholar
- Gough, B. J., & Stallman, R. M. (2005). An introduction to GCC. Network Theory LtdGoogle Scholar
- Harder, M., Mellen, J., & Ernst, M. D. (2003). Improving test suites via operational abstraction. In International conference on software engineering, pp. 60–71.Google Scholar
- Heimdahl, M. P. E., Rayadurgam, S., Visser, W., Devaraj, G., & Gao, J. (2003). Auto-generating test sequences using model checkers: A case study. In Workshop on formal approaches to testing of software, pp 42–59.Google Scholar
- Horgan, J. R., & London, S. (1991). Data flow coverage and the c language. In Proceedings of the symposium on testing, analysis, and verification, TAV4, pp. 87–97.Google Scholar
- Jaygarl, H., Lu, K. S., & Chang, C. K. (2010). Genred: A tool for generating and reducing object-oriented test cases. In IEEE annual computer software and applications conference, pp. 127–136.Google Scholar
- Martinian, E. (2010). Red-black tree c code. http://www.mit.edu/emin/source_code/red_black_tree.
- Masri, W., & Abou-Assi, R. (2010). Cleansing test suites from coincidental correctness to enhance fault-localization. In International conference on software testing, verification and validation, ICST ’10.Google Scholar
- McMillan, K. L. (1998). The smv language: Cadence berkeley labs. Technical report.Google Scholar
- Necula, G. C., Mcpeak, S., Rahul, S. P., & Weimer, W. (2002). Cil: Intermediate language and tools for analysis and transformation of c programs. In International conference on compiler, construction, pp. 213–228.Google Scholar
- PBCOV-APPENDICES. (2013). PBCOV-APPENDICES. http://webfea.fea.aub.edu.lb/fadi/dkwk/doku.php?id=pbcov.
- PBCOV-TOOL. (2013). PBCOV-TOOL. http://webfea.fea.aub.edu.lb/fadi/dkwk/doku.php?id=pbcov.
- Rapps, S., & Weyuker, E. J. (1982). Data flow analysis techniques for test data selection. In International conference on Software engineering (pp. 272–278). CA, USA: Los Alamitos.Google Scholar
- Santelices, R. A., Chittimalli, P. K., Apiwattanapong, T., Orso, A., & Harrold, M. J. (2008). Test-suite augmentation for evolving software. In ASE, pp 218–227.Google Scholar
- Schuler, D., & Zeller, A. (2011). Assessing oracle quality with checked coverage. In International conference on software testing, verification and validation, pp. 90–99.Google Scholar
- Torlak, E., & Jackson, D. (2007). Kodkod: A relational model finder. In Proceedings of the 13th international conference on tools and algorithms for the construction and analysis of systems, TACAS’07.Google Scholar
- Yang, J., & Evans, D. (2004). Dynamically inferring temporal properties. In SIGPLAN-SIGSOFT workshop on program analysis for software tools and engineering, PASTE ’04, pp 23–28.Google Scholar
- Zaraket, F., & Masri, W. (2009). Property based coverage criterion. In International workshop on defects in large software systems, DEFECTS ’09, pp. 27–28.Google Scholar