Skip to main content
SpringerLink
Log in

Multimedia in search-based software engineering: challenges and opportunities within a new research domain

  • 1179: Multimedia Software Engineering: Challenges and Opportunities
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Search-based software engineering (SBSE) is an emerging research sub-area in the field of software engineering. The concept of SBSE is based on the idea of formulation of software engineering problem as a search-based optimization problem and effective exploitation of metaheuristic search optimizers to solve it. The complex nature of software engineering problems and complex computational behaviour of the metaheuristic search algorithms makes the SBSE approaches challenging to understand and analyze. A variety of multimedia technologies are generally used to make the problem formulation and their computational method more understandable and analyzable. Even after wide application of multimedia in science and engineering, the SBSE got little attention in this direction. To explore and exploit the potential of the multimedia in the SBSE, this work first conducted a research study on the current trends of multimedia in SBSE, then based on this study, the various challenges and opportunities are presented. More specifically, our work mainly focusses on current multimedia trends in various forms of SBSE approaches (e.g., single, multi, and many-objective SBSE). Apart from that, we also explore the various opportunities and challenges in SBSE from the perspective of visualization of software artefacts, software quality metrics, problem formulation, search trajectory, Pareto optimal set and front.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Notes

  1. http://www.dependency-analyzer.org/

  2. https://structure101.com/

  3. https://sourceviz.wordpress.com/

References

  1. Harman M, Jones BF (2001)Search-based software engineering. Inf Softw Technol 43(14):833–839

    Article  Google Scholar 

  2. Harman M, Swift S, Mahdavi K (2005) An empirical study of the robustness of two module clustering fitness functions. In: Proceedings of the Conference on Genetic and Evolutionary Computation, pp. 1029–1036

  3. Harman M, Mansouri SA, Zhang Y (2012)Search-based software engineering: trends, techniques and applications. ACM Comput Surv Article 11 45(1):61

    Article  Google Scholar 

  4. Ramírez A, Romero JR, Ventura S (2019) A survey of many-objective optimisation in search-based software engineering. J Syst Softw 149:382–395

    Article  Google Scholar 

  5. Huang J, Liu J (2016) A similarity-based modularization quality measure for software module clustering problems. Inf Sci 342:96–110

    Article  Google Scholar 

  6. Pourasghar B, Izadkhah H, Isazadeh A, Lotfi S (2020) A graph-based clustering algorithm for software systems modularization Information and Software Technology, 106469, A graph-based clustering algorithm for software systems modularization, 133.

  7. Prajapati A, Chhabra JK (2017) Harmony search based remodularization for object-oriented software systems. Comput Lang Syst Struct 47(2):153–169

    Google Scholar 

  8. Jalali SN, Izadkhah H, Lotfi S (2019)Multi-objective search-based software modularization: structural and non-structural features. Soft Comput 23:11141–11165

    Article  Google Scholar 

  9. Praditwong K, Harman M, Yao X (2011) Software module clustering as a multi-objective search problem. IEEE Trans Softw Eng 37(2):264–282

    Article  Google Scholar 

  10. Prajapati A, Geem ZW (2020) Harmony search-based approach for multi-objective software architecture reconstruction. Mathematics 8(11):1906

    Article  Google Scholar 

  11. Mkaouer M, Kessentini M, Shaout A, Koligheu P, Bechikh S, Deb K, Ouni A (2015) Many objective software remodularization using NSGA-III. ACM Trans Softw Eng Methodol 24(3):1–17

    Article  Google Scholar 

  12. Prajapati A, Chhabra JK (2018) TA-ABC: two-archive artificial bee colony for multi-objective software module clustering problem. J Intell Syst 27(4):619–641

    Article  Google Scholar 

  13. Prajapati A, Chhabra JK (2018) FP-ABC: fuzzy pareto-dominance driven artificial bee colony algorithm for many objective software clustering. Comput Lang Syst Struct 51:1–21

    Google Scholar 

  14. Parashar A, Chhabra JK (2017)Package-restructuring based on software change history. Natl Acad Sci Lett 40:21–27

    Article  MathSciNet  Google Scholar 

  15. Knight C, Munro M (1999) Comprehension with[in] virtual environment visualisations. In: proceedings of the international workshop on program comprehension, IEEE computer society, pp. 4.

  16. Kot B, Wuensche B, Grundy J, Hosking J (2005) Information visualisation utilising 3d computer game engines - case study: A source code comprehension tool. In: Proceedings of the 6th Annual Conference of the ACM Special Interest Group on Compute Human Interaction, pp 53–60.

  17. Panas T, Berrigan R, Grundy J (2003) A 3D metaphor for software production visualization. In: Proceedings of the international conference on information visualization. IEEE Computer Society, pp. 314.

  18. Santos CRD, Gros P, Abel P, Loisel D, Trichaud N, Paris JP (2000) Mapping information onto 3d virtual worlds. In: Proceedings of the International Conference on Information Visualisation, 1, pp.379–386.

  19. Anslow C, Marshall S, Noble J, Biddle R (2006) VET3D: a tool for execution trace web 3D visualization. In: Proceedings of the ACM SIGPLAN symposium on object-oriented programming systems, languages, and applications (OOPSLA '06). ACM, pp 655–656.

  20. Anslow C, Marshall S, Noble J (2006)X3D-earth in the software visualization pipeline. In: Proceedings of the workshop on X3D earth requirements.

  21. Khaloo P, Maghoumi M, Taranta E, Bettner D, Laviola J (2017) Code Park: a new 3d code visualization tool. In: Proceedings of the Working Conference on Software Visualization. IEEE, pp. 43–53.

  22. Vincur J, Navrat P, Polasek I (2017) VR City: software analysis in a virtual reality environment. In: Proceedings of th international conference on software quality, reliability and security companion (QRS-C), pp 509-516.

  23. Merino L, Bergel A, Nierstrasz O (2018) Overcoming issues of 3d software visualization through immersive augmented reality. In: Proceedings of the IEEE Working Conference on Software Visualization, pp. 54–64.

  24. Fittkau F, Krause A, Hasselbring W (2017) Software landscape and application visualization for system comprehension with ExplorViz. Inf Softw Technol 87:259–277

    Article  Google Scholar 

  25. Hasselbring W, Krause, A and Zirkelbach C (2020) ExplorViz: Research on software visualization, comprehension and collaboration. Software Impacts, 6. https://doi.org/10.1016/j.simpa.2020.100034

  26. Doval D, Mancoridis S, Mitchell BS (1999) Automatic clustering of software systems using a genetic algorithm. In: Proceedings of the International Conference on Software Tools and Engineering Practice.

  27. Mancoridis S, Mitchell BS, Rorres C, Chen YF, Gansner ER (1998) Using automatic clustering to produce high-level system organizations of source code. In: proceedings of the international workshop on program comprehension (IWPC’98). IEEE computer society press, pp 45–53.

  28. Mahdavi K, Harman M, Hierons RM (2003) A multiple HillClimbing approach to software module clustering. In: Proceedings of the IEEE International Conference on Software Maintenance, pp. 315–324.

  29. Mitchell BS, Mancoridis S (2006) On the automatic modulariza-tion of software systems using the bunch tool. IEEE Trans Softw Eng 32(3):193–208

    Article  Google Scholar 

  30. Barros MO (2012) An analysis of the effects of composite objectives in multiobjective software module clustering. In: Proceedings of the Annual Conference on Genetic and Evolutionary computation (GECCO '12). Association for Computing Machinery, pp 1205–1212

  31. Kumari AC, Srinivas K (2016)Hyper-heuristic approach for multi-objective software module clustering. J Syst Softw 117:384–401

    Article  Google Scholar 

  32. Prajapati A, Chhabra JK (2014) An empirical study of the sensitivity of quality indicator for software module clustering. In: Proceedings of the IEEE seventh international conference on contemporary computing (IC3), pp 206-211

  33. Prajapati A, Chhabra JK (2019) MaDHS: many objective discrete harmony search to improve existing package design. Comput Intell 35:98–123

    Article  MathSciNet  Google Scholar 

  34. Prajapati A, Chhabra JK (2016) An efficient scheme for candidate solutions of search-based multi-objective software remodularization, Lecture Notes in Computer Science, pp 9734, 296–307.

  35. Inselberg A (1985) The plane with parallel coordinates. Vis Comput 1:69–91

    Article  MathSciNet  Google Scholar 

  36. Hoffman P, Grinstein G, Marx K, Grosse I, Stanley E (1997) DNA visual and analytic data mining, In: Proceedings of the Visualization, pp. 437–441.

  37. Kohonen T (2001)Self-organizing maps. Springer Series in Information Sciences, Berlin

    Book  Google Scholar 

  38. Ibrahim A, Rahnamayan S, Martin MV, Deb K (2016) 3D-RadVis: visualization of Pareto front in many-objective optimization. In: Evolutionary computation (CEC). IEEE congress, pp 736-745.

  39. Ibrahim A, Rahnamayan S, Martin MV, Deb K (2018)3D-RadVis antenna: visualization and performance measure for many-objective optimization. Swarm Evol Comput 39:157–176

    Article  Google Scholar 

  40. Kadluczka M, Nelson PC, Tirpak TM (2004)N-to-2 space mapping for visualization of search algorithm performance. In: Proceedings of the International Conference on Tools with Artificial Intelligence, pp. 508–513.

  41. Halim S, Wan WC, Lau HC (2005) Tuning Tabu Search strategies via visualdiagnosis. In: Proceedings of Metaheuristics International Conference, August 22-26, Vienna. pp. 630-636.

  42. Halim S, Yap RHC, Lau HC (2006) Visualization for analyzing trajectory-based metaheuristic search algorithms. In: Proceedings of the European Conference on Artificial Intelligence, pp. 703–704.

  43. Murray P, Forbes A (2014) StretchPlot: interactive visualization of multi-dimensional trajectory data. In: Proceedings of the IEEE conference on visual analytics science and technology (VAST), pp 261-262

  44. Perez J, Almanza N, Hidalgo M, Vela G, Alvarado L, García M, Mexicano A, Zavala C (2013) A graphical visualization tool for analyzing the behavior of metaheuristic algorithms. Int J Emerg Technol Adv Engi 3(4):32–36

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. JIIT, Noida, India

    Amarjeet Prajapati & Chakresh Kumar Jain

  2. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India

    Anshu Parashar

  3. CCET, Chandigarh, India

    Sunita

  4. NIT, Kurukshetra, India

    Jitender Kumar Chhabra

Authors
  1. Amarjeet Prajapati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anshu Parashar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sunita
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jitender Kumar Chhabra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chakresh Kumar Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anshu Parashar.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prajapati, A., Parashar, A., Sunita et al. Multimedia in search-based software engineering: challenges and opportunities within a new research domain. Multimed Tools Appl 81, 35671–35691 (2022). https://doi.org/10.1007/s11042-021-11882-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11882-0

Keywords

Search

Navigation