Skip to main content

A Preliminary Systematic Mapping Study of Human Competitiveness of SBSE

  • Conference paper
  • First Online:
Search-Based Software Engineering (SSBSE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11036))

Included in the following conference series:

Abstract

Search Based Software Engineering (SBSE) seeks to reformulate Software Engineering complex problems as search problems to be, hereafter, optimized through the usage of artificial intelligence techniques. As pointed out by Harman in 2007, in his seminal paper about the current state and future of SBSE, it would be very attractive to have convincing examples of human competitive results in order to champion the field. A landmark effort in this direction was made by Souza and others, in the paper titled “The Human Competitiveness of Search Based Software Engineering”, published at SSBSE’2010, voted by the SBSE community as the most influential paper of the past editions in the 10th anniversary of the SSBSE, in 2018. This paper presents a preliminary systematic mapping study to provide an overview of the current state of human competitiveness of SBSE, carried out via a snowball reading of Souza’s paper. The analyses of the 29 selected papers showed a growing interest in this topic, especially since 2010. Seven of those papers presented relevant experimental results, thus demonstrating the human competitiveness of results produced by SBSE approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://scholar.google.com/scholar?cites=12366459541329916171&as_sdt=2005.

References

  1. Harman, M., McMinn, P., de Souza, J.T., Yoo, S.: Search based software engineering: techniques, taxonomy, tutorial. In: Meyer, B., Nordio, M. (eds.) LASER 2008-2010. LNCS, vol. 7007, pp. 1–59. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-25231-0_1

    Chapter  Google Scholar 

  2. Harman, M.: The current state and future of search based software engineering. In: 2007 Future of Software Engineering, pp. 342–357. IEEE Computer Society (2007)

    Google Scholar 

  3. Harman, M.: Search based software engineering for program comprehension. In: 15th IEEE International Conference on Program Comprehension, ICPC 2007, pp. 3–13. IEEE (2007)

    Google Scholar 

  4. Koza, J.R.: Human-competitive results produced by genetic programming. Genet. Program. Evolvable Mach. 11(3–4), 251–284 (2010)

    Article  Google Scholar 

  5. Samuel, A.L.: AI, where it has been and where it is going. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 1152–1157 (1983)

    Google Scholar 

  6. Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Yu, J., Lanza, G.: Genetic Programming IV: Routine Human-Competitive Machine Intelligence, vol. 5. Springer, Heidelberg (2006). https://doi.org/10.1007/b137549

    Book  MATH  Google Scholar 

  7. Baker, P., Harman, M., Steinhofel, K., Skaliotis, A.: Search based approaches to component selection and prioritization for the next release problem. In: 22nd IEEE International Conference on Software Maintenance, ICSM 2006, pp. 176–185. IEEE (2006)

    Google Scholar 

  8. Yoo, S.: Evolving human competitive spectra-based fault localisation techniques. In: Fraser, G., Teixeira de Souza, J. (eds.) SSBSE 2012. LNCS, vol. 7515, pp. 244–258. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33119-0_18

    Chapter  Google Scholar 

  9. Xie, X., Kuo, F.-C., Chen, T.Y., Yoo, S., Harman, M.: Provably optimal and human-competitive results in SBSE for spectrum based fault localisation. In: Ruhe, G., Zhang, Y. (eds.) SSBSE 2013. LNCS, vol. 8084, pp. 224–238. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39742-4_17

    Chapter  Google Scholar 

  10. de Souza, J.T., Maia, C.L., de Freitas, F.G., Coutinho, D.P.: The human competitiveness of search based software engineering. In: Second International Symposium on Search Based Software Engineering, SSBSE 2010, pp. 143–152. IEEE (2010)

    Google Scholar 

  11. Kitchenham, B.: What’s up with software metrics?–a preliminary mapping study. J. Syst. Softw. 83(1), 37–51 (2010)

    Article  Google Scholar 

  12. Budgen, D., Turner, M., Brereton, P., Kitchenham, B.: Using mapping studies in software engineering. In: Proceedings of Psychology of Programming Interest Group (PPIG), vol. 8, pp. 195–204. Lancaster University (2008)

    Google Scholar 

  13. Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: International Conference on Evaluation and Assessment in Software Engineering, EASE 2008, vol. 8, pp. 68–77 (2008)

    Google Scholar 

  14. Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. MIS Q. xiii-xxiii (2002)

    Google Scholar 

  15. Wohlin, C.: Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, EASE 2014, p. 38. ACM (2014)

    Google Scholar 

  16. Colares, F., Souza, J., Carmo, R., Pádua, C., Mateus, G.R.: A new approach to the software release planning. In: XXIII Brazilian Symposium on Software Engineering, SBES 2009, pp. 207–215. IEEE (2009)

    Google Scholar 

  17. Harman, M.: The relationship between search based software engineering and predictive modeling. In: Proceedings of the 6th International Conference on Predictive Models in Software Engineering, PROMISE 2010, p. 1. ACM (2010)

    Google Scholar 

  18. Ren, J., Harman, M., Di Penta, M.: Cooperative co-evolutionary optimization of software project staff assignments and job scheduling. In: Cohen, M.B., Ó Cinnéide, M. (eds.) SSBSE 2011. LNCS, vol. 6956, pp. 127–141. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23716-4_14

    Chapter  Google Scholar 

  19. Zhang, Y., Harman, M., Finkelstein, A., Afshin Mansouri, S.: Comparing the performance of metaheuristics for the analysis of multi-stakeholder tradeoffs in requirements optimisation. Inf. Soft. Technol. 53(7), 761–773 (2011)

    Article  Google Scholar 

  20. Brasil, M.M.A., da Silva, T.G.N., de Freitas, F.G., de Souza, J.T., Cortés, M.I.: A multiobjective optimization approach to the software release planning with undefined number of releases and interdependent requirements. In: Zhang, R., Zhang, J., Zhang, Z., Filipe, J., Cordeiro, J. (eds.) ICEIS 2011. LNBIP, vol. 102, pp. 300–314. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29958-2_20

    Chapter  Google Scholar 

  21. Freitas, F.G., Coutinho, D.P., Souza, J.T.: Software next release planning approach through exact optimization. Int. J. Comput. Appl. (IJCA) 22(8), 1–8 (2011)

    Google Scholar 

  22. Vergilio, S.R., Colanzi, T.E., Pozo, A.T.R., Assunção, W.K.G.: Search based software engineering: a review from the Brazilian symposium on software engineering. In: 25th Brazilian Symposium on Software Engineering, SBES 2011, pp. 50–55. IEEE (2011)

    Google Scholar 

  23. Harman, M.: The role of artificial intelligence in software engineering. In: Proceedings of the First International Workshop on Realizing AI Synergies in Software Engineering, RAISE 2012, pp. 1–6. IEEE Press (2012)

    Google Scholar 

  24. Ramirez, A.J., Fredericks, E.M., Jensen, A.C., Cheng, B.H.C.: Automatically RELAXing a goal model to cope with uncertainty. In: Fraser, G., Teixeira de Souza, J. (eds.) SSBSE 2012. LNCS, vol. 7515, pp. 198–212. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33119-0_15

    Chapter  Google Scholar 

  25. Roshan, R., Porwal, R., Sharma, C.M.: Review of search based techniques in software testing. Int. J. Comput. Appl. (IJCA), 51(6) (2012)

    Google Scholar 

  26. Ali, S., Iqbal, M.Z., Arcuri, A., Briand, L.C.: Generating test data from ocl constraints with search techniques. IEEE Trans. Softw. Eng. 39(10), 1376–1402 (2013)

    Article  Google Scholar 

  27. Fraser, G., Staats, M., McMinn, P., Arcuri, A., Padberg, F.: Does automated white-box test generation really help software testers? In: International Symposium on Software Testing and Analysis, ISSTA 2013, pp. 291–301. ACM (2013)

    Google Scholar 

  28. Colanzi, T.E., Vergilio, S.R., Assunção, W.K.G., Pozo, A.: Search based software engineering: review and analysis of the field in Brazil. J. Syst. Softw. 86(4), 970–984 (2013)

    Article  Google Scholar 

  29. Yoo, S., Harman, M., Ur, S.: Gpgpu test suite minimisation: search based software engineering performance improvement using graphics cards. Empir. Softw. Eng. (ESE) 18(3), 550–593 (2013)

    Article  Google Scholar 

  30. Harman, M., Krinke, J., Medina-Bulo, I., Palomo-Lozano, F., Ren, J., Yoo, S.: Exact scalable sensitivity analysis for the next release problem. ACM Trans. Softw. Eng. Methodol. (TOSEM) 23(2), 19 (2014)

    Article  Google Scholar 

  31. Paixao, M.: A robust optimization approach to the next release problem in the presence of uncertainties (written in portuguese). Master’s thesis, Mestrado Acadêmico em Ciências da Computacão, Fortaleza (2014)

    Google Scholar 

  32. Fraser, G., Staats, M., McMinn, P., Arcuri, A., Padberg, F.: Does automated unit test generation really help software testers? A controlled empirical study. ACM Trans. Softw. Eng. Methodol. (TOSEM) 24(4), 23 (2015)

    Article  Google Scholar 

  33. do Nascimento Ferreira, T., Araújo, A.A., Neto, A.D.B., de Souza, J.T.: Incorporating user preferences in ant colony optimization for the next release problem. Appl. Soft Comput. 49, 1283–1296 (2016)

    Article  Google Scholar 

  34. Langdon, W.B., White, D.R., Harman, M., Jia, Y., Petke, J.: API-constrained genetic improvement. In: Sarro, F., Deb, K. (eds.) SSBSE 2016. LNCS, vol. 9962, pp. 224–230. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47106-8_16

    Chapter  Google Scholar 

  35. Ali, S., Iqbal, M.Z., Khalid, M., Arcuri, A.: Improving the performance of OCL constraint solving with novel heuristics for logical operations: a search-based approach. Empir. Softw. Eng. (ESE) 21(6), 2459–2502 (2016)

    Article  Google Scholar 

  36. Paixao, M., Harman, M., Zhang, Y., Yu, Y.: An empirical study of cohesion and coupling: balancing optimisation and disruption. IEEE Trans. Evol. Comput. (TEC) (2017)

    Google Scholar 

  37. Saeed, A., Hamid, S.H.A., Sani, A.A.: Cost and effectiveness of search-based techniques for model-based testing: an empirical analysis. Int. J. Softw. Eng. Knowl. Eng. (IJSEKE) 27(04), 601–622 (2017)

    Article  Google Scholar 

  38. Wu, F.: Mutation-based genetic improvement of software. Ph.D. thesis, UCL (University College London) (2017)

    Google Scholar 

  39. Mohan, M., Greer, D.: MultiRefactor: automated refactoring to improve software quality. In: Felderer, M., Méndez Fernández, D., Turhan, B., Kalinowski, M., Sarro, F., Winkler, D. (eds.) PROFES 2017. LNCS, vol. 10611, pp. 556–572. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69926-4_46

    Chapter  Google Scholar 

  40. Ali, A., Saeed, A.: Test case generation from state machine with OCL constraints using search-based techniques. Ph.D. thesis, University of Malaya (2017)

    Google Scholar 

  41. Ruhe, G., Wohlin, C.: Software project management: setting the context. In: Ruhe, G., Wohlin, C. (eds.) Software Project Management in a Changing World, pp. 1–24. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-55035-5_1

    Chapter  Google Scholar 

  42. Harman, M., Afshin Mansouri, S., Zhang, Y.: Search based software engineering: a comprehensive analysis and review of trends techniques and applications. Department of Computer Science, King’s College London, Technical report TR-09-03 (2009)

    Google Scholar 

  43. Zhang, Y., Harman, M., Afshin Mansouri, S.: The multi-objective next release problem. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, GECCO 2007, pp. 1129–1137. ACM (2007)

    Google Scholar 

  44. Fraser, G., Arcuri, A.: EvoSuite: automatic test suite generation for object-oriented software. In: Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, pp. 416–419. ACM (2011)

    Google Scholar 

  45. Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., Wesslén, A.: Experimentation in Software Engineering. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29044-2

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jerffeson Souza .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Souza, J., Araújo, A.A., Saraiva, R., Soares, P., Maia, C. (2018). A Preliminary Systematic Mapping Study of Human Competitiveness of SBSE. In: Colanzi, T., McMinn, P. (eds) Search-Based Software Engineering. SSBSE 2018. Lecture Notes in Computer Science(), vol 11036. Springer, Cham. https://doi.org/10.1007/978-3-319-99241-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99241-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99240-2

  • Online ISBN: 978-3-319-99241-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics