More Web Proxy on the site http://driver.im/

article

Consistency techniques for interprocedural test data generation

Authors:

Nguyen Tran Sy,

Yves DevilleAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 28, Issue 5

Pages 108 - 117

https://doi.org/10.1145/949952.940087

Published: 01 September 2003 Publication History

Abstract

This paper presents a novel approach for automated test data generation of imperative programs containing integer, boolean and/or float variables. It extends our previous work to programs with procedure calls and arrays. A test program (with procedure calls) is represented by an Interprocedural Control Flow Graph (ICFG). The classical testing criteria (statement, branch, and path coverage), widely used in unit testing, are extended to the ICFG. For path coverage, the specified path is transformed into a path constraint. Our previous consistency techniques, the core idea behind the solving of path constraints, have been extended to handle procedural calls and operations with arrays. For statement (and branch) coverage, paths reaching the specified node or branch are dynamically constructed. The search for suitable paths is guided by the interprocedural control dependences of the program. The search is also pruned by a new specialized consistency filter. Finally, test data are generated by the application of the proposed path coverage algorithm. A prototype has been implemented. Experiments show the feasibility of the approach.

References

[1]

L. Clarke. A system to generate test data and symbolically execute programs. IEEE Transactions on Software Engineering, 2(3):215--222, 1976.]]

[2]

J. Duran and S. Ntafos. An Evaluation of Random Testing. IEEE Transactions on Software Engineering, 10(4):438--444, July 1984.]]

Digital Library

[3]

R. Ferguson and B. Korel. The chaning approach for software test data generation. ACM Transactions on Software Engineering Methodology, 5(1):63--86, 1996.]]

Digital Library

[4]

J. Ferrante, K. J. Ottenstein, and J. D. Warren. The program dependence graph and its uses in optimization. ACMTransactions on Programming Languages and Systems, 9(3):319--349, July 1987.]]

Digital Library

[5]

A. Gotlieb, B. Botella, and M. Rueher. A CLP framework for computing structural test data. In Computational Logic, pages 399--413, 2000.]]

Digital Library

[6]

N. Gupta, A. P. Mathur, and M. L. Soffa. Automated test data generation using an iterative relaxation method. In ACM SIGSOFT Sixth International Symposium on Foundations of Software Engineering(FSE-6), Nov. 1998.]]

Digital Library

[7]

N. Gupta, A. P. Mathur, and M. L. Soffa. Generating test data for branch coverage. In 15th IEEE International Conference on Automated Software Engineering(ASE00), September 2000.]]

Digital Library

[8]

P. V. Hentenryck, L. Michel, and Y. Deville. Numerica. A modeling language for global optimization. The MIT Press, Cambridge, Massachusetts, London, 1997.]]

[9]

T. Hickey. An interval arithmetic library, 2000. http://interval.sourceforge.net/interval/index.html.]]

[10]

J. King. Symbolic Execution and Program Testing. Communications of the ACM, 19(7):385--394, July 1976.]]

Digital Library

[11]

B. Korel. Automated test data generation for programs with procedures. In Proceedings of the 1996 International Symposium on Software Testing and Analysis (ISSTA), pages 209--215, 1996.]]

Digital Library

[12]

D. Melski and T. W. Reps. Interprocedural path profiling. In Computational Complexity, 1999.]]

[13]

C. Michel, M. Rueher, and Y. Lebbah. Solving constraint over floating-point numbers. In Seventh International Conference on Principles and Practice of Constraint. Springer Verlag, LNCS, 2001.]]

Digital Library

[14]

R. Moore. Interval Analysis. Prentice-Hall, Englewood Cliffs, NJ, 1966.]]

[15]

R. P. Pargas, M. J. Harrold, and R. Peck. Test-data generation using genetic algorithms. Software Testing, Verification and Reliability, 9(4):263--282, 1999.]]

[16]

W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in C. The Art of Scientific Computing. Second Edition. Cambridge University Press, 1992.]]

Digital Library

[17]

D. J. Richardson and M. C. Thompson. An analysis of test data selection criteria using the RELAY model of fault detection. IEEE Transactions on Software Engineering, 19(6):533--553, June 1993.]]

Digital Library

[18]

G. Schumacher and A. Bantle. Automatic test case generation using interval arithmetic. In Proceedings of the SCAN2000/INTERVAL2000, Germany, 2000.]]

[19]

S. Sinha, M. J. Harrold, and G. Rothermel. Interprocedural control dependence. Software Engineering and Methodology, 10(2):209--254, 2001.]]

Digital Library

[20]

N. T. Sy and Y. Deville. Automatic test data generation for programs with integer and float variables. In 16th IEEE International Conference on Automated Software Engineering(ASE01), 2001.]]

Digital Library

[21]

E. Tsang. Foundations of Constraint Satisfaction. Academic Press, 1993.]]

Cited By

Hasheminasab ZSharifi ZSoltanian KAfsharchi M(2020)Using Augmented Genetic Algorithm for Search-Based Software TestingData Science: From Research to Application10.1007/978-3-030-37309-2_20(248-257)Online publication date: 29-Jan-2020
https://doi.org/10.1007/978-3-030-37309-2_20
Mann MTomar PSangwan O(2017)Test Data Generation Using Optimization Algorithm: An Empirical EvaluationSoft Computing: Theories and Applications10.1007/978-981-10-5699-4_64(679-686)Online publication date: 25-Nov-2017
https://doi.org/10.1007/978-981-10-5699-4_64
Mann MSangwan OTomar PSingh S(2016)Automatic goal-oriented test data generation using a Genetic algorithm and simulated annealing2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)10.1109/CONFLUENCE.2016.7508052(83-87)Online publication date: Jan-2016
https://doi.org/10.1109/CONFLUENCE.2016.7508052
Show More Cited By

Index Terms

Consistency techniques for interprocedural test data generation
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

Recommendations

Consistency techniques for interprocedural test data generation
ESEC/FSE-11: Proceedings of the 9th European software engineering conference held jointly with 11th ACM SIGSOFT international symposium on Foundations of software engineering

This paper presents a novel approach for automated test data generation of imperative programs containing integer, boolean and/or float variables. It extends our previous work to programs with procedure calls and arrays. A test program (with procedure ...
Strong mutation-based test data generation using hill climbing
SBST '16: Proceedings of the 9th International Workshop on Search-Based Software Testing

Mutation Testing is an effective test criterion for finding faults and assessing the quality of a test suite. Every test criterion requires the generation of test cases, which turns to be a manual and difficult task. In literature, search-based ...
Test data generation for covering mutation-based path using MGA for MPI program
Abstract
Message Passing Interface (MPI) is a communication protocol used for parallel programming in various languages, valued for its reliability and broad applicability. Mutation testing is a software testing method for systematically simulating ...
Highlights
- Group mutant branches into mutation-based paths for the parallel programs.
- The mutation-based paths can improve the efficiency of mutation testing.
- The mutation testing problem is transformed into an optimization problem.
- ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 28, Issue 5

September 2003

382 pages

ISSN:0163-5948

DOI:10.1145/949952

Issue’s Table of Contents

ESEC/FSE-11: Proceedings of the 9th European software engineering conference held jointly with 11th ACM SIGSOFT international symposium on Foundations of software engineering
September 2003
394 pages
ISBN:1581137435
DOI:10.1145/940071
General Chair:
Jukka Paakki
University of Helsinki, Finland
,
Program Chair:
Paola Inverardi
University of L'Aquila, Italy

Copyright © 2003 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 2003

Published in SIGSOFT Volume 28, Issue 5

Check for updates

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
636
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Hasheminasab ZSharifi ZSoltanian KAfsharchi M(2020)Using Augmented Genetic Algorithm for Search-Based Software TestingData Science: From Research to Application10.1007/978-3-030-37309-2_20(248-257)Online publication date: 29-Jan-2020
https://doi.org/10.1007/978-3-030-37309-2_20
Mann MTomar PSangwan O(2017)Test Data Generation Using Optimization Algorithm: An Empirical EvaluationSoft Computing: Theories and Applications10.1007/978-981-10-5699-4_64(679-686)Online publication date: 25-Nov-2017
https://doi.org/10.1007/978-981-10-5699-4_64
Mann MSangwan OTomar PSingh S(2016)Automatic goal-oriented test data generation using a Genetic algorithm and simulated annealing2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)10.1109/CONFLUENCE.2016.7508052(83-87)Online publication date: Jan-2016
https://doi.org/10.1109/CONFLUENCE.2016.7508052
Thi DHieu VHa N(2016)A Technique for Generating Test Data Using Genetic Algorithm2016 International Conference on Advanced Computing and Applications (ACOMP)10.1109/ACOMP.2016.019(67-73)Online publication date: Nov-2016
https://doi.org/10.1109/ACOMP.2016.019
Juan ZWeiqin TLizhi C(2012)An Evaluation Model in Software Testing Based on AHPProceedings of the 2012 IEEE/ACIS 11th International Conference on Computer and Information Science10.1109/ICIS.2012.31(601-604)Online publication date: 30-May-2012
https://dl.acm.org/doi/10.1109/ICIS.2012.31
Chen YZhong Y(2009)Experimental study on GA-based path-oriented test data generation using branch distanceProceedings of the 3rd international conference on Intelligent information technology application10.5555/1794554.1794608(216-219)Online publication date: 21-Nov-2009
https://dl.acm.org/doi/10.5555/1794554.1794608
Chen YZhong Y(2009)Experimental Study on GA-Based Path-Oriented Test Data Generation Using Branch Distance FunctionProceedings of the 2009 Third International Symposium on Intelligent Information Technology Application - Volume 0110.1109/IITA.2009.232(216-219)Online publication date: 21-Nov-2009
https://dl.acm.org/doi/10.1109/IITA.2009.232
Chen YZhong YShi TLiu J(2009)Comparison of Two Fitness Functions for GA-Based Path-Oriented Test Data GenerationProceedings of the 2009 Fifth International Conference on Natural Computation - Volume 0410.1109/ICNC.2009.235(177-181)Online publication date: 14-Aug-2009
https://dl.acm.org/doi/10.1109/ICNC.2009.235
Chen YZhong Y(2008)Automatic Path-Oriented Test Data Generation Using a Multi-population Genetic AlgorithmProceedings of the 2008 Fourth International Conference on Natural Computation - Volume 0110.1109/ICNC.2008.388(566-570)Online publication date: 18-Oct-2008
https://dl.acm.org/doi/10.1109/ICNC.2008.388
Chen JZhu LShen JWang Z(2006)A scheme on automated test data generation and its evaluationJournal of Central South University of Technology10.1007/s11771-006-0112-713:1(87-92)Online publication date: Feb-2006
https://doi.org/10.1007/s11771-006-0112-7
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents