Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1109/FOSE.2007.29guideproceedingsArticle/Chapter ViewAbstractPublication PagesicseConference Proceedingsconference-collections
Article
Free access

The Current State and Future of Search Based Software Engineering

Published: 23 May 2007 Publication History

Abstract

This paper describes work on the application of optimization techniques in software engineering. These optimization techniques come from the operations research and metaheuristic computation research communities. The paper briefly reviews widely used optimization techniques and the key ingredients required for their successful application to software engineering, providing an overview of existing results in eight software engineering application domains. The paper also describes the benefits that are likely to accrue from the growing body of work in this area and provides a set of open problems, challenges and areas for future work.

References

[1]
{1} J. Aguilar-Ruiz, I. Ramos, J. C. Riquelme, and M. Toro. An evolutionary approach to estimating software development rojects. Information and Software Technology, 43(14):875-882, Dec. 2001.
[2]
{2} E. Alba and J. F. Chicano. Observations in using parallel and sequential evolutionary algorithms for automatic software testing. Computers and Operations Research (COR) focused issue on Search Based Software Engineeering. to appear.
[3]
{3} G. Antoniol, M. Di Penta, and M. Harman. A robust search-based approach to project management in the presence of abandonment, rework, error and uncertainty. In 10th International Software Metrics Symposium (METRICS 2004), pages 172-183, Los Alamitos, California, USA, Sept. 2004. IEEE Computer Society Press.
[4]
{4} G. Antoniol, M. D. Penta, and M. Harman. Search-based techniques applied to optimization of project planning for a massive maintenance project. In 21st IEEE International Conference on Software Maintenance, pages 240-249, Los Alamitos, California, USA, 2005. IEEE Computer Society Press.
[5]
{5} A. Bagnall, V. Rayward-Smith, and I. Whittley. The next release problem. Information and Software Technology, 43(14):883-890, Dec. 2001.
[6]
{6} A. Baresel, D. W. Binkley, M. Harman, and B. Korel. Evolutionary testing in the presence of loop-assigned flags: A testability transformation approach. In International Symposium on Software Testing and Analysis (ISSTA 2004), pages 108-118, Omni Parker House Hotel, Boston, Massachusetts, July 2004. Appears in Software Engineering Notes, Volume 29, Number 4.
[7]
{7} A. Baresel, H. Sthamer, and M. Schmidt. Fitness function design to improve evolutionary structural testing. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1329-1336, San Francisco, CA 94104, USA, 9-13 July 2002. Morgan Kaufmann Publishers.
[8]
{8} A. Barreto, M. Barros, and C. Werner. Staffing a sowftare project: A constraint satisfaction and optimization based approach. Computers and Operations Research (COR) focused issue on Search Based Software Engineering.
[9]
{9} T. V. Belle and D. H. Ackley. Code factoring and the evolution of evolvability. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1383-1390, San Francisco, CA 94104, USA, 9-13 July 2002. Morgan Kaufmann Publishers.
[10]
{10} A. Bertolino. Software testing research: Achievements, challenges, dreams. In L. Briand and A. Wolf, editors, Future of Software Engineering 2007, Los Alamitos, California, USA, 2007. IEEE Computer Society Press. This volume.
[11]
{11} L. Bottaci. Instrumenting programs with flag variables for test data search by genetic algorithms. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1337-1342, New York, 9-13 July 2002. Morgan Kaufmann Publishers.
[12]
{12} S. Bouktif, G. Antoniol, E. Merlo, and M. Neteler. A novel approach to optimize clone refactoring activity. In GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, volume 2, pages 1885-1892, Seattle, Washington, USA, 8-12 July 2006. ACM Press.
[13]
{13} S. Bouktif, H. Sahraoui, and G. Antoniol. Simulated annealing for improving software quality prediction. In GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, volume 2, pages 1893-1900, Seattle, Washington, USA, 8-12 July 2006. ACM Press.
[14]
{14} L. C. Briand, J. Feng, and Y. Labiche. Using genetic algorithms and coupling measures to devise optimal integration test orders. In SEKE, pages 43-50, 2002.
[15]
{15} L. C. Briand, Y. Labiche, and M. Shousha. Stress testing real-time systems with genetic algorithms. In Genetic and Evolutionary Computation Conference, GECCO 2005, Proceedings, Washington DC, USA, June 25-29, 2005, pages 1021-1028. ACM, 2005.
[16]
{16} C. J. Burgess and M. Lefley. Can genetic programming improve software effort estimation? A comparative evaluation. Information and Software Technology, 43(14):863- 873, Dec. 2001.
[17]
{17} E. Burke and G. Kendall. Search Methodologies. Introductory tutorials in optimization and decision support techniques . Springer, 2005.
[18]
{18} G. Canfora and M. Di Penta. New frontiers in reverse engineering. In L. Briand and A. Wolf, editors, Future of Software Engineering 2007, Los Alamitos, California, USA, 2007. IEEE Computer Society Press. This volume.
[19]
{19} G. Canfora, M. D. Penta, R. Esposito, and M. L. Villani. An approach for qoS-aware service composition based on genetic algorithms. In H.-G. Beyer and U.-M. O'Reilly, editors, Genetic and Evolutionary Computation Conference, GECCO 2005, Proceedings, Washington DC, USA, June 25- 29, 2005, pages 1069-1075. ACM, 2005.
[20]
{20} B. Cheng and J. Atlee. From state of the art to the future of requirements engineering. In L. Briand and A. Wolf, editors, Future of Software Engineering 2007, Los Alamitos, California, USA, 2007. IEEE Computer Society Press. This volume.
[21]
{21} F. Chicano and E. Alba. Management of software projects with gas. In 6th Metaheuristics International Conference (MIC2005), Vienna, Austria, Aug. 2005.
[22]
{22} J. Clark, J. J. Dolado, M. Harman, R. M. Hierons, B. Jones, M. Lumkin, B. Mitchell, S. Mancoridis, K. Rees, M. Roper, and M. Shepperd. Reformulating software engineering as a search problem. IEE Proceedings -- Software, 150(3):161- 175, 2003.
[23]
{23} E. J. Coffman, M. Garey, and D. Johnson. Approximation algorithms for bin-packing. In Algorithm Design for Computer System Design, 1984.
[24]
{24} M. Cohen, S. B. Kooi, and W. Srisa-an. Clustering the heap in multi-threaded applications for improved garbage collection. In GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, volume 2, pages 1901-1908, Seattle, Washington, USA, 8-12 July 2006. ACM Press.
[25]
{25} K. D. Cooper, P. J. Schielke, and D. Subramanian. Optimizing for reduced code space using genetic algorithms. In Proceedings of the ACMSigplan 1999 Workshop on Languages, Compilers and Tools for Embedded Systems (LCTES'99), volume 34.7 of ACM Sigplan Notices, pages 1-9, NY, May 5 1999. ACM Press.
[26]
{26} V. Cortellessa, F. Marinelli, and P. Potena. An optimization framework for "build-or-buy" decisions in sowftare architecture. Computers and Operations Research (COR) focused issue on Search Based Software Engineeering.
[27]
{27} C. Del Grosso, G. Antoniol, E. Merlo, and P. Galinier. Detecting buffer overflow via automatic test input data generation. Computers and Operations Research (COR) focused issue on Search Based Software Engineeering.
[28]
{28} J. J. Dolado. A validation of the component-based method for software size estimation. IEEE Transactions on Software Engineering, 26(10):1006-1021, 2000.
[29]
{29} J. J. Dolado. On the problem of the software cost function. Information and Software Technology, 43(1):61-72, Jan. 2001.
[30]
{30} M. Dorigo and C. Blum. Ant colony optimization theory: A survey. Theoretical Computer Science, 344(2-3):243-278, 2005.
[31]
{31} D. Fatiregun, M. Harman, and R. Hierons. Evolving transformation sequences using genetic algorithms. In 4th International Workshop on Source Code Analysis and Manipulation (SCAM 04), pages 65-74, Los Alamitos, California, USA, Sept. 2004. IEEE Computer Society Press.
[32]
{32} D. Fatiregun, M. Harman, and R. Hierons. Search-based amorphous slicing. In 12th International Working Conference on Reverse Engineering (WCRE 05), pages 3- 12, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA, Nov. 2005.
[33]
{33} P. Funes, E. Bonabeau, J. Herve, and Y. Morieux. Interactive multi-participant task allocation. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation, pages 1699- 1705, Portland, Oregon, 20-23 June 2004. IEEE Press.
[34]
{34} N. Gold, M. Harman, Z. Li, and K. Mahdavi. A search based approach to overlapping concept boundaries. In 22nd International Conference on Software Maintenance (ICSM 06), Philadelphia, Pennsylvania, USA, Sept. 2006. To appear.
[35]
{35} D. Greer and G. Ruhe. Software release planning: an evolutionary and iterative approach. Information & Software Technology, 46(4):243-253, 2004.
[36]
{36} Q. Guo, R. M. Hierons, M. Harman, and K. Derderian. Constructing multiple unique input/output sequences using evolutionary optimisation techniques. IEE Proceedings--Software , 152(3):127-140, 2005.
[37]
{37} M. Harman and J. Clark. Metrics are fitness functions too. In 10th International Software Metrics Symposium (METRICS 2004), pages 58-69, Los Alamitos, California, USA, Sept. 2004. IEEE Computer Society Press.
[38]
{38} M. Harman, R. Hierons, and M. Proctor. A new representation and crossover operator for search-based optimization of software modularization. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1351-1358, San Francisco, CA 94104, USA, 9-13 July 2002. Morgan Kaufmann Publishers.
[39]
{39} M. Harman, L. Hu, R. M. Hierons, J. Wegener, H. Sthamer, A. Baresel, and M. Roper. Testability transformation. IEEE Transactions on Software Engineering, 30(1):3-16, Jan. 2004.
[40]
{40} M. Harman and B. F. Jones. Search based software engineering. Information and Software Technology, 43(14):833-839, Dec. 2001.
[41]
{41} M. Harman, K. Steinhöfel, and A. Skaliotis. Search based approaches to component selection and prioritization for the next release problem. In 22nd International Conference on Software Maintenance (ICSM 06), Philadelphia, Pennsylvania, USA, Sept. 2006. To appear.
[42]
{42} M. Harman, S. Swift, and K. Mahdavi. An empirical study of the robustness of two module clustering fitness functions. In Genetic and Evolutionary Computation Conference (GECCO 2005), pages 1029-1036, Washington DC, USA, June 2005. Association for Computer Machinery.
[43]
{43} M. Harman and J. Wegener. Evolutionary testing: Tutorial. In Genetic and Evolutionary Computation (GECCO), Chicago, July 2003.
[44]
{44} M. Harman and J. Wegener. Getting results with search-based software engineering: Tutorial. In 26th IEEE International Conference and Software Engineering (ICSE 2004), pages 728-729, Los Alamitos, California, USA, 2004. IEEE Computer Society Press.
[45]
{45} M. Harman and J. Wegener. Search based testing. In 6th Metaheuristics International Conference (MIC 2005), Vienna, Austria, Aug. 2005. To appear.
[46]
{46} E. Hart and P. Ross. GAVEL - a new tool for genetic algorithm visualization. IEEE-EC, 5:335-348, Aug. 2001.
[47]
{47} J. H. Holland. Adaption in Natural and Artificial Systems. MIT Press, Ann Arbor, 1975.
[48]
{48} J. Karlsson, C. Wohlin, and B. Regnell. An evaluation of methods for priorizing software requirements. Information and Software Technology, 39:939-947, 1998.
[49]
{49} T. M. Khoshgoftaar, L. Yi, and N. Seliya. A multiobjective module-order model for software quality enhancement. IEEE Transactions on Evolutionary Computation, 8(6):593- 608, December 2004.
[50]
{50} Y.-H. Kimand B.-R. Moon. Visualization of the fitness landscape, A steady-state genetic search, and schema traces. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, page 686, San Francisco, CA 94104, USA, 9-13 July 2002. Morgan Kaufmann Publishers.
[51]
{51} Y.-H. Kim and B.-R. Moon. New usage of sammon's mapping for genetic visualization. In Genetic and Evolutionary Computation - GECCO-2003, volume 2723 of LNCS, pages 1136-1147, Berlin, 12-16 July 2003. Springer-Verlag.
[52]
{52} C. Kirsopp, M. Shepperd, and J. Hart. Search heuristics, case-based reasoning and software project effort prediction. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1367-1374, San Francisco, CA 94104, USA, 9-13 July 2002. Morgan Kaufmann Publishers.
[53]
{53} J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, 1992.
[54]
{54} Z. Li, M. Harman, and R. Hierons. Meta-heuristic search algorithms for regression test case prioritization. IEEE Transactions on Software Engineering. To appear.
[55]
{55} D. S. Linden. Innovative antenna design using genetic algorithms. In D. W. Corne and P. J. Bentley, editors, Creative Evolutionary Systems, chapter 20. Elsevier, Amsterdam, The Netherland, 2002.
[56]
{56} R. Lutz. Evolving good hierarchical decompositions of complex systems. Journal of Systems Architecture, 47:613-634, 2001.
[57]
{57} K. Mahdavi, M. Harman, and R.M. Hierons. A multiple hill climbing approach to software module clustering. In IEEE International Conference on Software Maintenance, pages 315-324, Los Alamitos, California, USA, Sept. 2003. IEEE Computer Society Press.
[58]
{58} S. Mancoridis, B. S.Mitchell, Y.-F. Chen, and E. R. Gansner. Bunch: A clustering tool for the recovery and maintenance of software system structures. In Proceedings; IEEE International Conference on Software Maintenance, pages 50- 59. IEEE Computer Society Press, 1999.
[59]
{59} S. Mancoridis, B. S. Mitchell, C. Rorres, Y.-F. Chen, and E. R. Gansner. Using automatic clustering to produce high-level system organizations of source code. In International Workshop on Program Comprehension (IWPC'98), pages 45-53, Los Alamitos, California, USA, 1998. IEEE Computer Society Press.
[60]
{60} P. McMinn. Search-based software test data generation: A survey. Software Testing, Verification and Reliability, 14(2):105-156, June 2004.
[61]
{61} P. McMinn, M. Harman, D. Binkley, and P. Tonella. The species per path approach to search-based test data generation. In International Symposium on Software Testing and Analysis (ISSTA 06), pages 13-24, Portland, Maine, USA., 2006.
[62]
{62} N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller. Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21:1087-1092, 1953.
[63]
{63} B. S. Mitchell. A Heuristic Search Approach to Solving the Software Clustering Problem. PhD Thesis, Drexel University, Philadelphia, PA, Jan. 2002.
[64]
{64} B. S. Mitchell and S. Mancoridis. Using heuristic search techniques to extract design abstractions from source code. In GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1375-1382, San Francisco, CA 94104, USA, 9-13 July 2002. Morgan Kaufmann Publishers.
[65]
{65} B. S. Mitchell and S. Mancoridis. On the automatic modularization of software systems using the bunch tool. IEEE Transactions on Software Engineering, 32(3):193- 208, 2006.
[66]
{66} F. Mueller and J. Wegener. A comparison of static analysis and evolutionary testing for the verification of timing constraints. In 4th IEEE Real-Time Technology and Applications Symposium (RTAS '98), pages 144-154, Washington - Brussels - Tokyo, June 1998. IEEE.
[67]
{67} H. Mühlenbein and G. Paaß. From recombination of genes to the estimation of distributions: I. Binary parameters. In H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature - PPSN IV, pages 178-187, Berlin, 1996. Springer.
[68]
{68} Nashat Mansour, Rami Bahsoon and G. Baradhi. Empirical comparison of regression test selection algorithms. Systems and Software, 57(1):79-90, 2001.
[69]
{69} A. Nisbet. GAPS: A compiler framework for genetic algorithm (GA) optimised parallelisation. In P. M. A. Sloot, M. Bubak, and L. O. Hertzberger, editors, High-Performance Computing and Networking, International Conference and Exhibition, HPCN Europe 1998, Amsterdam, The Netherlands, April 21-23, 1998, Proceedings, volume LNCS 1401, pages 987-989. Springer, 1998.
[70]
{70} M. O'Keeffe and M. O'Cinneide. Search-based software maintenance. In Conference on Software Maintenance and Reengineering (CSMR'06), pages 249-260, Mar. 2006.
[71]
{71} H. Pohlheim. Visualization of evolutionary algorithms - set of standard techniques and multidimensional visualization. In Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, pages 533-540, San Francisco, CA 94104, USA, July 1999. Morgan Kaufmann.
[72]
{72} G. Rothermel, S. Elbaum, A. G. Malishevsky, P. Kallakuri, and X. Qiu. On test suite composition and cost-effective regression testing. ACM Trans. Softw. Eng. Methodol., 13(3):277-331, 2004.
[73]
{73} G. Rothermel, R. Untch, C. Chu, and M. J. Harrold. Prioritizing test cases for regression testing. IEEE Transactions on Software Engineering, 27(10):929-948, Oct. 2001.
[74]
{74} C. Ryan. Automatic re-engineering of software using genetic programming. Kluwer Academic Publishers, 2000.
[75]
{75} T. Schnier, X. Yao, and P. Liu. Digital filter design using multiple pareto fronts. Soft Computing, 8(5):332-343, April 2004.
[76]
{76} H. Schwefel and T. Bäck. Artificial evolution: How and why? In D. Quagliarella, J. Périaux, C. Poloni, and G. Winter, editors, Genetic Algorithms and Evolution Strategy in Engineering and Computer Science, pages 1-19. John Wiley and Sons, 1998.
[77]
{77} O. Seng, M. Bauer, M. Biehl, and G. Pache. Search-based improvement of subsystem decompositions. In H.-G. Beyer and U.-M. O'Reilly, editors, Genetic and Evolutionary Computation Conference, GECCO 2005, Proceedings, Washington DC, USA, June 25-29, 2005, pages 1045-1051. ACM, 2005.
[78]
{78} O. Seng, J. Stammel, and D. Burkhart. Search-based determination of refactorings for improving the class structure of object-oriented systems. In GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation , volume 2, pages 1909-1916, Seattle, Washington, USA, 8-12 July 2006. ACM Press.
[79]
{79} C. E. Shannon. A mathematical theory of communication. Bell System Technical Journal, 27:379-423 and 623-656, July and October 1948.
[80]
{80} M. Shepperd. Software economics. In L. Briand and A. Wolf, editors, Future of Software Engineering 2007, Los Alamitos, California, USA, 2007. IEEE Computer Society Press. This volume.
[81]
{81} M. J. Shepperd. Foundations of software measurement. Prentice Hall, 1995.
[82]
{82} M. J. Shepperd and C. Schofield. Estimating software project effort using analogies. IEEE Transactions on Software Engineering, 23(11):736-743, 1997.
[83]
{83} M. Tang and J. Dong. Simulated annealing genetic algorithm for surface intersection. In Advances in Natural Computation , volume 3612 of Lecture Notes in Computer Science , pages 48-56. Springer, Aug. 2005.
[84]
{84} N. Tracey, J. Clark, and K. Mander. Automated program flaw finding using simulated annealing. In International Symposium on Software Testing and Analysis (ISSTA 98), pages 73-81, March 1998.
[85]
{85} K. R. Walcott, M. L. Soffa, G. M. Kapfhammer, and R. S. Roos. Time aware test suite prioritization. In International Symposium on Software Testing and Analysis (ISSTA 06), pages 1 - 12, Portland, Maine, USA., 2006. ACM Press.
[86]
{86} S. Wappler and J. Wegener. Evolutionary unit testing of object-oriented software using strongly-typed genetic programming. In GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, volume 2, pages 1925-1932, Seattle, Washington, USA, 8-12 July 2006. ACM Press.
[87]
{87} J. Wegener, A. Baresel, and H. Sthamer. Evolutionary test environment for automatic structural testing. Information and Software Technology Special Issue on Software Engineering using Metaheuristic Innovative Algorithms, 43(14):841-854, 2001.
[88]
{88} K. P. Williams. Evolutionary Algorithms for Automatic Parallelization . PhD thesis, University of Reading, UK, Department of Computer Science, Sept. 1998.
[89]
{89} S. Xanthakis, C. Ellis, C. Skourlas, A. Le Gall, S. Katsikas, and K. Karapoulios. Application of genetic algorithms to software testing (Application des algorithmes génétiques au test des logiciels). In 5th International Conference on Software Engineering and its Applications, pages 625-636, Toulouse, France, 1992.
[90]
{90} S. Yoo. The use of a novel semi-exhaustive search algorithm for the analysis of data sensitivity in a feature subset selection problem. Master's thesis, King's College London, Department of Computer Science, 2006.
[91]
{91} X. Zhang, H. Meng, and L. Jiao. Intelligent particle swarm optimization in multiobjective optimization. In Proceedings of the 2005 IEEE Congress on Evolutionary Computation, volume 1, pages 714-719, Edinburgh, UK, Sept. 2005. IEEE Press.

Cited By

View all

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings
FOSE '07: 2007 Future of Software Engineering
May 2007
382 pages
ISBN:0769528295

Publisher

IEEE Computer Society

United States

Publication History

Published: 23 May 2007

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)20
  • Downloads (Last 6 weeks)6
Reflects downloads up to 20 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2024)MMO: Meta Multi-Objectivization for Software Configuration TuningIEEE Transactions on Software Engineering10.1109/TSE.2024.338891050:6(1478-1504)Online publication date: 15-Apr-2024
  • (2024)Evaluating Search-Based Software Microbenchmark PrioritizationIEEE Transactions on Software Engineering10.1109/TSE.2024.338083650:7(1687-1703)Online publication date: 1-Jul-2024
  • (2024)Coevolutionary scheduling of dynamic software project considering the new skill learningAutomated Software Engineering10.1007/s10515-023-00411-y31:1Online publication date: 19-Jan-2024
  • (2023)Baldur: Whole-Proof Generation and Repair with Large Language ModelsProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616243(1229-1241)Online publication date: 30-Nov-2023
  • (2023)The Weights Can Be Harmful: Pareto Search versus Weighted Search in Multi-objective Search-based Software EngineeringACM Transactions on Software Engineering and Methodology10.1145/351423332:1(1-40)Online publication date: 13-Feb-2023
  • (2023)Coverage-directed Differential Testing of X.509 Certificate Validation in SSL/TLS ImplementationsACM Transactions on Software Engineering and Methodology10.1145/351041632:1(1-32)Online publication date: 22-Feb-2023
  • (2022)Diversity-driven automated formal verificationProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510138(749-761)Online publication date: 21-May-2022
  • (2021)Facebook’s Cyber–Cyber and Cyber–Physical Digital TwinsProceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering10.1145/3463274.3463275(1-9)Online publication date: 21-Jun-2021
  • (2021)Information Reuse and Stochastic SearchACM Transactions on Autonomous and Adaptive Systems10.1145/344011915:1(1-36)Online publication date: 1-Feb-2021
  • (2020)An Effective Regression Test Case Selection Using Hybrid Whale Optimization AlgorithmInternational Journal of Distributed Systems and Technologies10.4018/IJDST.202001010511:1(53-67)Online publication date: 1-Jan-2020
  • Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media