Using Fuzzy Logic Techniques for Assertion-Based Software Testing Metrics

Software testing is a very labor intensive and costly task. Therefore, many software testing techniques to automate the process of software testing have been reported in the literature. Assertion-Based automated software testing has been shown to be effective in detecting program faults as compared to traditional black-box and white-box software testing methods. However, the applicability of this approach in the presence of large numbers of assertions may be very costly. Therefore, software developers need assistance while making decision to apply Assertion-Based testing in order for them to get the benefits of this approach at an acceptable level of costs. In this paper, we present an Assertion-Based testing metrics technique that is based on fuzzy logic. The main goal of the proposed technique is to enhance the performance of Assertion-Based software testing in the presence of large numbers of assertions. To evaluate the proposed technique, an experimental study was performed in which the proposed technique is applied on programs with assertions. The result of this experiment shows that the effectiveness and performance of Assertion-Based software testing have improved when applying the proposed testing metrics technique.

[1]  Matthew J. Rutherford,et al.  An Empirical Evaluation of Assertions as Oracles , 2011, 2011 Fourth IEEE International Conference on Software Testing, Verification and Validation.

[2]  Thanh Binh Nguyen,et al.  Testing criteria for data flow software , 2003, Tenth Asia-Pacific Software Engineering Conference, 2003..

[3]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[4]  Bogdan Korel,et al.  The chaining approach for software test data generation , 1996, TSEM.

[5]  Kai Petersen,et al.  Benefits and limitations of automated software testing: Systematic literature review and practitioner survey , 2012, 2012 7th International Workshop on Automation of Software Test (AST).

[6]  Bogdan Korel,et al.  Assertion-oriented automated test data generation , 1996, Proceedings of IEEE 18th International Conference on Software Engineering.

[7]  Alexander L. Wolf,et al.  Evaluating Test Suites and Adequacy Criteria Using Simulation-Based Models of Distributed Systems , 2008, IEEE Transactions on Software Engineering.

[8]  Gary Riley,et al.  Expert Systems: Principles and Programming , 2004 .

[9]  Bogdan Korel,et al.  Dynamic method for software test data generation , 1992, Softw. Test. Verification Reliab..

[10]  K. Karnavel,et al.  Automated software testing for application maintenance by using bee colony optimization algorithms (BCO) , 2013, 2013 International Conference on Information Communication and Embedded Systems (ICICES).

[11]  Bogdan Korel,et al.  Automated Software Test Data Generation , 1990, IEEE Trans. Software Eng..

[12]  M. K. Debbarma,et al.  Static and dynamic software metrics complexity analysis in regression testing , 2012, 2012 International Conference on Computer Communication and Informatics.

[13]  Thomas Reidemeister,et al.  Efficient Fault Detection and Diagnosis in Complex Software Systems with Information-Theoretic Monitoring , 2011, IEEE Transactions on Dependable and Secure Computing.

[14]  Ali M. Alakeel.  An Algorithm for Efficient Assertions-Based Test Data Generation , 2010, J. Softw..

[15]  Mauro Pezzè,et al.  Software testing and analysis - process, principles and techniques , 2007 .

[16]  Xiaojun Wu,et al.  The Study on an Intelligent General-Purpose Automated Software Testing Suite , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[17]  Lijun Lun,et al.  Path numbers analysis of relationships on software architecture testing criteria , 2010, 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).

[18]  Praveen Ranjan Srivastava,et al.  Automated Software Testing Using Metahurestic Technique Based on an Ant Colony Optimization , 2010, 2010 International Symposium on Electronic System Design.

[19]  Graham A. Stephen.  String Searching Algorithms , 1994, Lecture Notes Series on Computing.

[20]  Colin J. Fidge,et al.  Automatic Generation of Assertions to Detect Potential Security Vulnerabilities in C Programs That Use Union and Pointer Types , 2010, 2010 Fourth International Conference on Network and System Security.

[21]  Arvinder Kaur,et al.  Empirical validation of object-oriented metrics for predicting fault proneness models , 2010, Software Quality Journal.

[22]  Glenford J. Myers,et al.  Art of Software Testing , 1979 .

[23]  David S. Rosenblum.  A Practical Approach to Programming With Assertions , 1995, IEEE Trans. Software Eng..

[24]  Porshia Mitra,et al.  Graphical analysis of MC/DC using automated software testing , 2011, 2011 3rd International Conference on Electronics Computer Technology.