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Data Analysis for Software Process Improvement: A Systematic Literature Review

  • Jezreel MejíaEmail author
  • Freddy Íñiguez
  • Mirna Muñoz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 569)

Abstract

Current information systems demand high quality software products that guarantee a safety and a reliable use for our day-to-day life. A common understanding between software organizations and practitioners is that software product quality largely depends on the software process quality. A Software Process Improvement (SPI) initiative consists of a set of practices and activities that are designed to improve software organizations processes through the evaluation of their current practices and the way software products and services are developed. However, the big amount of information that is generated from the software organization practices has complicated the knowledge extraction, and therefore, the SPI initiatives. A possible technique to make a good knowledge management is data analysis. This paper presents the results of a systematic literature review to establish the state-of-the-art of data analysis for software process improvement. The findings also encourage to the creation of a BigData-based data analysis model in a future work for this research.

Keywords

Knowledge management Information management Data analysis Software product quality Software process quality Software engineering 

References

  1. 1.
    O’Regan, J.: Introduction to software process improvement. J. Chem. Inf. Model. 53(9), 1689–1699 (2013)Google Scholar
  2. 2.
    Mejia, J., Muñoz, E., Muñoz, M.: Reinforcing the applicability of multi-model environments for software process improvement using knowledge management. Sci. Comput. Program. 121, 3–15 (2016)CrossRefGoogle Scholar
  3. 3.
    Chugh, M., Chugh, N., Punia, D. K.: Evaluation and analysis of knowledge management best practices in software process improvement a multicase experience. In: Second International Conference on Advances in Computing and Communication Engineering (ICACCE), pp. 661–666 (2015)Google Scholar
  4. 4.
    Kuhrmann, M., Konopka, C., Nellemann, P., Diebold, P., Münch, J.: Software process improvement: where is the evidence? initial findings from a systematic mapping study. In: Proceedings of the 2015 International Conference on Software and System Process, pp. 107–116 (2015)Google Scholar
  5. 5.
    Tsai, C.F., Yeh, H.F., Chang, J.F., Liu, N.H.: PHD: an efficient data clustering scheme using partition space technique for knowledge discovery in large databases. Appl. Intell. 33(1), 39–53 (2010)CrossRefGoogle Scholar
  6. 6.
    Kitchenham, B.: Systematic reviews. In: 10th International Symposium on Software Metrics (2004)Google Scholar
  7. 7.
    DeLine, R.: Research opportunities for the big data era of software engineering. In: 1st International Workshop on Big Data Software Engineering, BIGDSE (2015)Google Scholar
  8. 8.
    Söylemez, M., Tarhan, A.: Using process enactment data analysis to support orthogonal defect classification for software process improvement. In: Joint Conference of the 23rd IWSM-MENSURA (2013)Google Scholar
  9. 9.
    Rao, J., Kelappan, R., & Pallath, P.: Recommendation system to enhance planning of software development using R (2014)Google Scholar
  10. 10.
    Zheng, L., Zeng, C., Li, L., Jiang, Y., Xue, W., Li, J., Wang, P.: Applying data mining techniques to address critical process optimization needs in advanced manufacturing (2014)Google Scholar
  11. 11.
    Grabova, O., Darmont, J., Chauchat, J., Zolotaryova, I.: Business intelligence for small and middle-sized enterprises (2010)Google Scholar
  12. 12.
    Mazón, J., Zubcoff, J., Garrigós, I., Espinosa, R., Rodríguez, R.: Open business intelligence: on the importance of data quality awareness in user-friendly data mining (2012)Google Scholar
  13. 13.
    Baysal, O.: Informing development decisions: From data to information. In: International Conference on Software Engineering (2013)Google Scholar
  14. 14.
    Sureka, A., Kumar, A., Gupta, S.: Ahaan: Software process intelligence: mining software process data for extracting actionable information (2015)Google Scholar
  15. 15.
    Ivarsson, M., Gorschek, T.: Tool support for disseminating and improving development practices. Softw. Qual. J. 20, 173–199 (2012)CrossRefGoogle Scholar
  16. 16.
    Shibata, T., Kurachi, Y.: Big data analysis solutions for driving innovation in on-site decision making. Fujitsu Sci. Technol. J. 51(2), 33–41 (2015)Google Scholar
  17. 17.
    Vera, A., Colomo, R., Molloy, O.: Real-time business activity monitoring and analysis of process performance on big-data domains. Telematics Inform. (2015)Google Scholar
  18. 18.
    Pavon, R., Carpenter, B.: Synthesis of decision making: from data to business execution (2013)Google Scholar
  19. 19.
    Leida, M., Majeed, B., Colombo, M., Chu, A.: A lightweight RDF data model for business process analysis. In: Cudre-Mauroux, P., Ceravolo, P., Gašević, D. (eds.) SIMPDA 2012. LNBIP, vol. 162, pp. 1–23. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40919-6_1 CrossRefGoogle Scholar
  20. 20.
    Fazzinga, B., Flesca, S., Furfaro, F., Masciari, E., Pontieri, L.: A compression-based framework for the efficient analysis of business process logs (2015)Google Scholar
  21. 21.
    Bertini, E., Lalanne, D.: Investigating and reflecting on the integration of automatic data analysis and visualization in knowledge discovery (2010)Google Scholar
  22. 22.
    Chang, C., Lin, T.: The role of organizational culture in the knowledge management process. J. Knowl. Manage. 19(3), 433–455 (2015)CrossRefGoogle Scholar
  23. 23.
    Diedrich, A., Guzman, G.: From implementation to appropriation: understanding knowledge management system development and introduction as a process of translation. J. Knowl. Manage. 19(6), 1273–1294 (2015)CrossRefGoogle Scholar
  24. 24.
    Balco, P. Drahoova, M.: Knowledge management as a service (KMaaS). In: 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), pp. 57–62 (2016)Google Scholar
  25. 25.
    Lee, K., Chen, Y., Muñoz, C.: Examining the impact of organizational culture and top management support of knowledge sharing on the success of software process improvement. Comput. Hum. Behav. 54, 462–474 (2016)CrossRefGoogle Scholar
  26. 26.
    Lihua, L., Feifei, Y.: Knowledge management in high technology enterprises. In: 2010 International Conference on E-Business and E-Government, pp. 1823–1826 (2010)Google Scholar
  27. 27.
    Cuesta, H.: Practical Data Analysis. Packt Publishing, Birmingham (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jezreel Mejía
    • 1
    Email author
  • Freddy Íñiguez
    • 1
  • Mirna Muñoz
    • 1
  1. 1.Center for Research in MathematicsZacatecasMexico

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