Abstract
This paper presents Specstractor, a tool chain for the extraction and analysis of system specifications in the form of collections of invariants. Such invariants convey valuable information about the behavior of a software system and are also useful in identifying missing or defective parts of existing specifications. Using data-mining techniques, Specstractor derives likely invariants from test data that it automatically generates from the system under analysis, using an iterative approach to refine the set of proposed invariants and eliminate false positives. The paper describes the Spectstractor technology and evaluates it on real-world artifacts from automotive-control and medical-device applications.
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Notes
- 1.
In fact, it is common practice in these applications not to use C-function inputs and outputs, but instead use other external variables for this purpose.
- 2.
Reactis® and Reactis for C® are registered trademarks of Reactive Systems, Inc.
References
Ackermann, C., Cleaveland, R., Huang, S., Ray, A., Shelton, C., Latronico, E.: Automatic requirement extraction from test cases. In: Barringer, H., Falcone, Y., Finkbeiner, B., Havelund, K., Lee, I., Pace, G., Roşu, G., Sokolsky, O., Tillmann, N. (eds.) RV 2010. LNCS, vol. 6418, pp. 1–15. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16612-9_1
Ernst, M.D., et al.: The Daikon system for dynamic detection of likely invariants. Sci. Comput. Program. 69, 35–45 (2007)
Cheng, X., Hsiao, M.S.: Simulation-directed invariant mining for software verification. In: Proceedings of DATE 2008, pp. 682–687. ACM, New York (2008)
Beschastnikh, I., et al.: Mining temporal invariants from partially ordered logs. In: SLAML 2011, pp. 3:1–3:10. ACM, New York (2011)
Schulze, C., Cleaveland, R.: Improving invariant mining via static analysis. ACM Trans. Embed. Comput. Syst. 16, 167:1–167:20 (2017)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD Record, vol. 29, pp. 1–12. ACM (2000)
Fournier-Viger, P., Lin, J.C.-W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., Lam, H.T.: The SPMF open-source data mining library version 2. In: Berendt, B., Bringmann, B., Fromont, É., Garriga, G., Miettinen, P., Tatti, N., Tresp, V. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9853, pp. 36–40. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46131-1_8
Cleaveland, R., Smolka, S.A., Sims, S.T.: An instrumentation-based approach to controller model validation. In: Broy, M., Krüger, I.H., Meisinger, M. (eds.) ASWSD 2006. LNCS, vol. 4922, pp. 84–97. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70930-5_6
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. Proc. VLDB 1215, 487–499 (1994)
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: Proceedings of SIGMOD, pp. 1–12. ACM, New York (1996)
Salleb-Aouissi, A., Vrain, C., Nortet, C.: Quantminer: a genetic algorithm for mining quantitative association rules. IJCAI 7, 1035–1040 (2007)
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, New York (2011)
Bay, S.D.: Multivariate discretization for set mining. Knowl. Inf. Syst. 3(4), 491–512 (2001)
Kellerer, H., Pferschy, U., Pisinger, D.: Introduction to NP-completeness of knapsack problems. In: Knapsack Problems, pp. 483–493. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24777-7_16
Lindvall, M., et al.: Safety-focused security requirements elicitation for medical device software. In: Requirements Engineering, pp. 134–143. IEEE (2017)
Merten, M., Steffen, B., Howar, F., Margaria, T.: Next generation learnlib. In: Abdulla, P.A., Leino, K.R.M. (eds.) TACAS 2011. LNCS, vol. 6605, pp. 220–223. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19835-9_18
Rozier, K.Y.: Specification: the biggest bottleneck in formal methods and autonomy. In: Blazy, S., Chechik, M. (eds.) VSTTE 2016. LNCS, vol. 9971, pp. 8–26. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48869-1_2
Zeller, A.: Specifications for free. In: Bobaru, M., Havelund, K., Holzmann, G.J., Joshi, R. (eds.) NFM 2011. LNCS, vol. 6617, pp. 2–12. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20398-5_2
Dallmeier, V., Knopp, N., Mallon, C., Hack, S., Zeller, A.: Generating test cases for specification mining. In: Proceedings of ISSTA, p. 85. ACM Press, July 2010
Le Goues, C., Weimer, W.: Specification mining with few false positives. In: Kowalewski, S., Philippou, A. (eds.) TACAS 2009. LNCS, vol. 5505, pp. 292–306. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00768-2_26
Lorenzoli, D., Mariani, L., Pezzè, M.: Automatic generation of software behavioral models. In: Proceedings of ICSE, p. 501. ACM Press, May 2008
Vasudevan, S., et al.: GoldMine: automatic assertion generation using data mining and static analysis. In: Proceedings of DATE, pp. 626–629 (2010)
Zeng, F., Cao, Q., Mao, L., Chen, Z.: Test case generation based on invariant extraction. In: Proceedings of WCNMC, pp. 1–4. IEEE, September 2009
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Schulze, C., Cleaveland, R., Lindvall, M. (2018). Automated Specification Extraction and Analysis with Specstractor. In: Johnsen, E., Schaefer, I. (eds) Software Engineering and Formal Methods. SEFM 2018. Lecture Notes in Computer Science(), vol 10886. Springer, Cham. https://doi.org/10.1007/978-3-319-92970-5_3
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