Similar articles

Abstract: Static analysis is an efficient approach for software assurance. It is indicated that its most effective usage is to perform analysis in an interactive way through the software development process, which has a high performance requirement. This paper concentrates on rule-based static analysis tools and proposes an optimized rule-checking algorithm. Our technique improves the performance of static analysis tools by filtering vulnerability rules in terms of characteristic objects before checking source files. Since a source file always contains vulnerabilities of a small part of rules rather than all, our approach may achieve better performance. To investigate our technique’s feasibility and effectiveness, we implemented it in an open source static analysis tool called PMD and used it to conduct experiments. Experimental results show that our approach can obtain an average performance promotion of 28.7% compared with the original PMD. While our approach is effective and precise in detecting vulnerabilities, there is no side effect.

基于改进规则检查静态分析技术的高效脆弱性检测方法

[1]Alpuente, M., Feliú, M.A., Joubert, C., et al., 2009. Using Datalog and Boolean equation systems for program analysis. 13th Int. Workshop on Formal Methods for Industrial Critical Systems, p.215-231.

[2]Araújo, J.E.M., Souza, S., Valente, M.T., 2011. Study on the relevance of the warnings reported by Java bug-finding tools. IET Softw., 5(4):366-374.

[3]Atkinson, D.C., Griswold, W.G., 2006. Effective pattern matching of source code using abstract syntax patterns. Softw.-Pract. Exp., 36(4):413-447.

[4]Ayewah, N., Pugh, W., Morgenthaler, J.D., et al., 2007. Evaluating static analysis defect warnings on production software. ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools & Engineering, p.1-8.

[5]Ball, T., 2008. The verified software challenge: a call for a holistic approach to reliability. LNCS, 4171:42-48.

[6]Bounimova, E., Godefroid, P., Molnar, D., 2013. Billions and billions of constraints: whitebox fuzz testing in production. 35th Int. Conf. on Software Engineering, p.122-131.

[7]Bovet, J., Parr, T., 2008. ANTLRWorks: an ANTLR grammar development environment. Softw.-Pract. Exp., 38(12): 1305-1332.

[8]Chen, D., Huang, R., Qu, B., et al., 2014. Improving static analysis performance using rule-filtering technique. 26th Int. Conf. on Software Engineering and Knowledge Engineering, p.19-24.

[9]Emanuelsson, P., Nilsson, U., 2008. A comparative study of industrial static analysis tools. Electron. Notes Theor. Comput. Sci., 217:5-21.

[10]Engler, D., Chen, D.Y., Hallem, S., et al., 2001. Bugs as deviant behavior: a general approach to inferring errors in systems code. ACM SIGOPS Oper. Syst. Rev., 35(5):57-72.

[11]Hajiyev, E., Verbaere, M., de Moor, O., 2006. CodeQuest: scalable source code queries with Datalog. 20th European Conf. on Object-Oriented Programming, p.2-27.

[12]Haydar, M., Petrenko, A., Boroday, S., et al., 2013. A formal approach for run-time verification of web applications using scope-extended LTL. Inform. Softw. Technol., 55(12):2191-2208.

[13]Helmick, M.T., 2007. Interface-based programming assignments and automatic grading of Java programs. 12th Annual SIGCSE Conf. on Innovation & Technology in Computer Science Education, p.63-67.

[14]Hovemeyer, D., Pugh, W., 2004. Finding bugs is easy. ACM SIGPLAN Not., 39(12):92-106.

[15]Hovemeyer, D., Pugh, W., 2007. Finding more null pointer bugs, but not too many. ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools & Engineering, p.9-14.

[16]Jarzabek, S., 1998. Design of flexible static program analyzers with PQL. IEEE Trans. Softw. Eng., 24(3):197-215.

[17]Liu, S., Zhang, R., Wang, D., et al., 2008. Implementing of Gaussian syntax-analyzer using ANTLR. Int. Conf. on Cyberworlds, p.613-618.

[18]Loveland, S., 2009. Using open source tools to prevent write-only code. 6th Int. Conf. on Information Technology: New Generations, p.671-677.

[19]Martin, M., Livshits, B., Lam, M.S., 2005. Finding application errors and security flaws using PQL: a program query language. ACM SIGPLAN Not., 40(10):365-383.

[20]Panchenko, O., Treffer, A., Zeier, A., 2010. Towards query formulation and visualization of structural search results. ICSE Workshop on Search-Driven Development: Users, Infrastructure, Tools and Evaluation, p.33-36.

[21]Panchenko, O., Karstens, J., Plattner, H., et al., 2011. Precise and scalable querying of syntactical source code patterns using sample code snippets and a database. 19th Int. Conf. on Program Comprehension, p.41-50.

[22]Parr, T., Fisher, K., 2011. LL(*): the foundation of the ANTLR parser generator. ACM SIGPLAN Not., 46(6):425-436.

[23]Plosch, R., Gruber, H., Hentschel, A., et al., 2008. On the relation between external software quality and static code analysis. 32nd Annual IEEE Software Engineering Workshop, p.169-174.

[24]Rajamani, S.K., 2006. Automatic property checking for software: past, present and future. 4th IEEE Int. Conf. on Software Engineering and Formal Methods, p.18-20.

[25]Reinbacher, T., Brauer, J., Horauer, M., et al., 2014. Runtime verification of microcontroller binary code. Sci. Comput. Program., 80(A):109-129.

[26]Rutar, N., Almazan, C.B., Foster, J.S., 2004. A comparison of bug finding tools for Java. 15th Int. Symp. on Software Reliability Engineering, p.245-256.

[27]Schaps, G.L., 1999. Compiler construction with ANTLR and Java—tools for building tools. Dr. Dobb’s J., 24(3):84-89.

[28]Whaley, J., Avots, D., Carbin, M., et al., 2005. Using Datalog with binary decision diagrams for program analysis. Asian Symp. on Programming Languages and Systems, p.97-118.

[29]Zook, D., Pasalic, E., Sarna-Starosta, B., 2009. Typed datalog. LCNS, 5418:168-182.