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Cross Fertilization in Software Engineering

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Systems, Software and Services Process Improvement (EuroSPI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1251))

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Abstract

Software engineering is related to a set of disciplines both inside and outside computing. One of the aspects to consider in the development of a discipline is cross fertilization. In this paper, author reviews the cross fertilization produced by related disciplines in Software Engineering. The influences come from Computer Engineering and Computer Science inside computing outside this field quality and project management, naming just a few of them. More in particular by focusing on specific technologies, author will overview bidirectional relationships between two of the most promising technologies nowadays namely: blockchain and machine learning.

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References

  1. Abran, A., Fairley, D.: SWEBOK: GUIDE to the Software Engineering Body of Knowledge Version 3. IEEE Computer Society, Los Alamitos (2014)

    Google Scholar 

  2. Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2020)

    Google Scholar 

  3. Alvargonzález, D.: Multidisciplinarity, interdisciplinarity, transdisciplinarity, and the sciences. Int. Stud. Philos. Sci. 25(4), 387–403 (2011). https://doi.org/10.1080/02698595.2011.623366

    Article  Google Scholar 

  4. Angelis, J., Ribeiro da Silva, E.: Blockchain adoption: a value driver perspective. Bus. Horiz. 62(3), 307–314 (2019). https://doi.org/10.1016/j.bushor.2018.12.001

  5. Ardis, M., et al.: SE 2014: curriculum guidelines for undergraduate degree programs in software engineering. Computer 48(11), 106–109 (2015). https://doi.org/10.1109/MC.2015.345

    Article  Google Scholar 

  6. Azeem, M.I., et al.: Machine learning techniques for code smell detection: a systematic literature review and meta-analysis. Inf. Softw. Technol. 108, 115–138 (2019). https://doi.org/10.1016/j.infsof.2018.12.009

    Article  Google Scholar 

  7. Bai, C., Sarkis, J.: A supply chain transparency and sustainability technology appraisal model for blockchain technology. Int. J. Prod. Res. 58, 2142–2162 (2020). https://doi.org/10.1080/00207543.2019.1708989

    Article  Google Scholar 

  8. Beller, M., Hejderup, J.: Blockchain-based software engineering. In: Proceedings of the 41st International Conference on Software Engineering: New Ideas and Emerging Results, pp. 53–56. IEEE Press, Montreal (2019). https://doi.org/10.1109/ICSE-NIER.2019.00022

  9. Broy, M.: Mathematics of software engineering. In: Möller, B. (ed.) MPC 1995. LNCS, vol. 947, pp. 18–48. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60117-1_3

    Chapter  Google Scholar 

  10. Chakraborty, P., et al.: Understanding the software development practices of blockchain projects: a survey. In: Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, pp. 1–10. Association for Computing Machinery, Oulu (2018). https://doi.org/10.1145/3239235.3240298

  11. Coblenz, M.: Obsidian: a safer blockchain programming language. In: 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C). pp. 97–99 (2017). https://doi.org/10.1109/ICSE-C.2017.150

  12. Curtis, B.: Fifteen years of psychology in software engineering: individual differences and cognitive science. In: Proceedings of the 7th International Conference on Software Engineering, pp. 97–106. IEEE Press, Orlando (1984)

    Google Scholar 

  13. Di Francesco Maesa, D., Mori, P.: Blockchain 3.0 applications survey. J. Parallel Distrib. Comput. 138, 99–114 (2020). https://doi.org/10.1016/j.jpdc.2019.12.019

  14. Dogan, M.: Creative Marginality: Innovation at the Intersections of Social Sciences. Routledge, Abingdon (2019)

    Google Scholar 

  15. Dogan, M., Pahre, R.: Fragmentation and recombination of the social sciences. Stud. Comp. Int. Dev. 24(2), 56–72 (1989). https://doi.org/10.1007/BF02687172

    Article  Google Scholar 

  16. Domik, G., Fischer, G.: Coping with complex real-world problems: strategies for developing the competency of transdisciplinary collaboration. In: Reynolds, N., Turcsányi-Szabó, M. (eds.) KCKS 2010. IAICT, vol. 324, pp. 90–101. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15378-5_9

    Chapter  Google Scholar 

  17. Dougherty, J.P.: MATH COUNTS: where mathematics meets software engineering. ACM Inroads 8(3), 13–15 (2017). https://doi.org/10.1145/3123734

    Article  Google Scholar 

  18. Dybå, T., et al.: Qualitative research in software engineering. Empir. Softw. Eng. 16(4), 425–429 (2011). https://doi.org/10.1007/s10664-011-9163-y

    Article  Google Scholar 

  19. Efanov, D., Roschin, P.: The all-pervasiveness of the blockchain technology. Procedia Comput. Sci. 123, 116–121 (2018). https://doi.org/10.1016/j.procs.2018.01.019

    Article  Google Scholar 

  20. França, A.C.C., et al.: Motivation in software engineering industrial practice: a cross-case analysis of two software organisations. Inf. Softw. Technol. 56(1), 79–101 (2014). https://doi.org/10.1016/j.infsof.2013.06.006

    Article  Google Scholar 

  21. Gren, L., et al.: The perceived effects of group developmental psychology training on agile software development teams. IEEE Softw. (2019). https://doi.org/10.1109/MS.2019.2955675

  22. Hoda, R., et al.: Socio-cultural challenges in global software engineering education. IEEE Trans. Educ. 60(3), 173–182 (2017). https://doi.org/10.1109/TE.2016.2624742

    Article  Google Scholar 

  23. Hu, T., et al.: An evolutionary learning and network approach to identifying key metabolites for osteoarthritis. PLOS Comput. Biol. 14(3), e1005986 (2018). https://doi.org/10.1371/journal.pcbi.1005986

  24. Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015). https://doi.org/10.1126/science.aaa8415

    Article  MathSciNet  MATH  Google Scholar 

  25. Juristo, N., Moreno, A.M.: Basics of Software Engineering Experimentation. Springer, Heidelberg (2013)

    Google Scholar 

  26. Kazman, R., Pasquale, L.: Software engineering in society. IEEE Softw. 37(1), 7–9 (2020). https://doi.org/10.1109/MS.2019.2949322

    Article  Google Scholar 

  27. Khomh, F., et al.: Software engineering for machine-learning applications: the road ahead. IEEE Softw. 35(5), 81–84 (2018). https://doi.org/10.1109/MS.2018.3571224

    Article  Google Scholar 

  28. Knight, J.C., Leveson, N.G.: Should software engineers be licensed? Commun. ACM 45(11), 87–90 (2002)

    Article  Google Scholar 

  29. Kolb, J., et al.: Core concepts, challenges, and future directions in blockchain: a centralized tutorial. ACM Comput. Surv. 53(1), 9:1–9:39 (2020). https://doi.org/10.1145/3366370

  30. Król, M., et al.: ChainSoft: collaborative software development using smart contracts. In: Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems, pp. 1–6. ACM, New York (2018). https://doi.org/10.1145/3211933.3211934

  31. Lao, L., et al.: A survey of IoT applications in blockchain systems: architecture, consensus, and traffic modeling. ACM Comput. Surv. 53(1), 18:1–18:32 (2020). https://doi.org/10.1145/3372136

  32. Leahey, E., Reikowsky, R.C.: Research specialization and collaboration patterns in sociology. Soc. Stud. Sci. 38(3), 425–440 (2008). https://doi.org/10.1177/0306312707086190

    Article  Google Scholar 

  33. LeBlanc, R.J., Sobel, A.: Software Engineering 2014: Curriculum Guidelines for Undergraduate Degree Programs in Software Engineering. IEEE Computer Society, Los Alamitos (2014)

    Google Scholar 

  34. Lenarduzzi, V., et al.: Blockchain applications for agile methodologies. In: Proceedings of the 19th International Conference on Agile Software Development: Companion, pp. 1–3. Association for Computing Machinery, Porto (2018). https://doi.org/10.1145/3234152.3234155

  35. Madni, A.M.: Transdisciplinarity: reaching beyond disciplines to find connections. J. Integr. Des. Process Sci. 11(1), 1–11 (2007)

    Google Scholar 

  36. Malhotra, R.: A systematic review of machine learning techniques for software fault prediction. Appl. Soft Comput. 27, 504–518 (2015). https://doi.org/10.1016/j.asoc.2014.11.023

    Article  Google Scholar 

  37. Marchesi, M., et al.: An agile software engineering method to design blockchain applications. In: Proceedings of the 14th Central and Eastern European Software Engineering Conference Russia, pp. 1–8. Association for Computing Machinery, Moscow (2018). https://doi.org/10.1145/3290621.3290627

  38. Marsland, S.: Machine Learning: An Algorithmic Perspective, 2nd edn. CRC Press, Boca Raton (2015)

    Google Scholar 

  39. Matsubara, T., Ebert, C.: Guest editor’s introduction: benefits and applications of cross-pollination. IEEE Softw. 17(1), 24 (2000)

    Article  Google Scholar 

  40. Memeti, S., Pllana, S., Binotto, A., Kołodziej, J., Brandic, I.: Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review. Computing 101(8), 893–936 (2018). https://doi.org/10.1007/s00607-018-0614-9

    Article  MathSciNet  Google Scholar 

  41. Mendes, F.F., et al.: The relationship between personality and decision-making: a systematic literature review. Inf. Softw. Technol. 111, 50–71 (2019). https://doi.org/10.1016/j.infsof.2019.03.010

    Article  Google Scholar 

  42. Méndez Fernández, D., Passoth, J.-H.: Empirical software engineering: from discipline to interdiscipline. J. Syst. Softw. 148, 170–179 (2019). https://doi.org/10.1016/j.jss.2018.11.019

    Article  Google Scholar 

  43. Niazi, M., et al.: Software process improvement barriers: a cross-cultural comparison. Inf. Softw. Technol. 52(11), 1204–1216 (2010). https://doi.org/10.1016/j.infsof.2010.06.005

    Article  Google Scholar 

  44. Novielli, N., Serebrenik, A.: Sentiment and emotion in software engineering. IEEE Softw. 36(5), 6–23 (2019). https://doi.org/10.1109/MS.2019.2924013

    Article  Google Scholar 

  45. Parizi, R.M., et al.: Empirical vulnerability analysis of automated smart contracts security testing on blockchains. In: Proceedings of the 28th Annual International Conference on Computer Science and Software Engineering, pp. 103–113. IBM Corp., Markham (2018)

    Google Scholar 

  46. Parnas, D.: Software engineering - missing in action: a personal perspective. Computer 44(10), 54–58 (2011). https://doi.org/10.1109/MC.2011.268

    Article  Google Scholar 

  47. Porru, S., et al.: Blockchain-oriented software engineering: challenges and new directions. In: 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C), pp. 169–171 (2017). https://doi.org/10.1109/ICSE-C.2017.142

  48. Rocha, H., Ducasse, S.: Preliminary steps towards modeling blockchain oriented software. In: Proceedings of the 1st International Workshop on Emerging Trends in Software Engineering for Blockchain, pp. 52–57. ACM, New York (2018). https://doi.org/10.1145/3194113.3194123

  49. Sánchez-Gordón, M., Colomo-Palacios, R.: Taking the emotional pulse of software engineering—a systematic literature review of empirical studies. Inf. Softw. Technol. 115, 23–43 (2019). https://doi.org/10.1016/j.infsof.2019.08.002

    Article  Google Scholar 

  50. Sharp, H., et al.: Models of motivation in software engineering. Inf. Softw. Technol. 51(1), 219–233 (2009). https://doi.org/10.1016/j.infsof.2008.05.009

    Article  Google Scholar 

  51. Singi, K., et al.: Compliance adherence in distributed software delivery: a blockchain approach. In: 2018 IEEE/ACM 13th International Conference on Global Software Engineering (ICGSE), pp. 126–127 (2018)

    Google Scholar 

  52. Siris, V.A., et al.: Interledger approaches. IEEE Access 7, 89948–89966 (2019). https://doi.org/10.1109/ACCESS.2019.2926880

    Article  Google Scholar 

  53. Smith, A.J.: Applications of the self-organising map to reinforcement learning. Neural Netw. 15(8), 1107–1124 (2002). https://doi.org/10.1016/S0893-6080(02)00083-7

    Article  Google Scholar 

  54. Soomro, A.B., et al.: The effect of software engineers’ personality traits on team climate and performance: a systematic literature review. Inf. Softw. Technol. 73, 52–65 (2016). https://doi.org/10.1016/j.infsof.2016.01.006

    Article  Google Scholar 

  55. Wang, N., et al.: When energy trading meets blockchain in electrical power system: the state of the art. Appl. Sci. 9(8), 1561 (2019). https://doi.org/10.3390/app9081561

  56. Wang, P., et al.: QoS-aware service composition using blockchain-based smart contracts. In: Proceedings of the 40th International Conference on Software Engineering: Companion Proceedings, pp. 296–297. ACM, New York (2018). https://doi.org/10.1145/3183440.3194978

  57. Wen, J., et al.: Systematic literature review of machine learning based software development effort estimation models. Inf. Softw. Technol. 54(1), 41–59 (2012). https://doi.org/10.1016/j.infsof.2011.09.002

    Article  Google Scholar 

  58. Wenger, E.: Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press, Cambridge (1999)

    Google Scholar 

  59. Wessling, F., Gruhn, V.: Engineering software architectures of blockchain-oriented applications. In: 2018 IEEE International Conference on Software Architecture Companion (ICSA-C), pp. 45–46 (2018). https://doi.org/10.1109/ICSA-C.2018.00019

  60. Wrobel, M.R.: Applicability of emotion recognition and induction methods to study the behavior of programmers. Appl. Sci. 8(3), 323 (2018). https://doi.org/10.3390/app8030323

  61. Xie, S., et al.: Blockchain for cloud exchange: a survey. Comput. Electr. Eng. 81, 106526 (2020). https://doi.org/10.1016/j.compeleceng.2019.106526

    Article  Google Scholar 

  62. Yilmaz, M., et al.: An examination of personality traits and how they impact on software development teams. Inf. Softw. Technol. 86, 101–122 (2017). https://doi.org/10.1016/j.infsof.2017.01.005

    Article  Google Scholar 

  63. Yilmaz, M., Tasel, S., Tuzun, E., Gulec, U., O’Connor, R.V., Clarke, P.M.: Applying blockchain to improve the integrity of the software development process. In: Walker, A., O’Connor, R.V., Messnarz, R. (eds.) EuroSPI 2019. CCIS, vol. 1060, pp. 260–271. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28005-5_20

    Chapter  Google Scholar 

  64. Zheng, Z., et al.: An overview on smart contracts: challenges, advances and platforms. Future Gener. Comput. Syst. 105, 475–491 (2020). https://doi.org/10.1016/j.future.2019.12.019

    Article  Google Scholar 

  65. Zheng, Z., et al.: Blockchain challenges and opportunities: a survey. Int. J. Web Grid Serv. 14(4), 352–375 (2018). https://doi.org/10.1504/IJWGS.2018.095647

    Article  Google Scholar 

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Colomo-Palacios, R. (2020). Cross Fertilization in Software Engineering. In: Yilmaz, M., Niemann, J., Clarke, P., Messnarz, R. (eds) Systems, Software and Services Process Improvement. EuroSPI 2020. Communications in Computer and Information Science, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-56441-4_1

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  • DOI: https://doi.org/10.1007/978-3-030-56441-4_1

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