Data Analysis for Software Process Improvement: A Systematic Literature Review

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.

Appendix 1: List of Primary Studies

• [PS-01] Baysal, O. (2013). Informing development decisions: From data to information. International Conference on Software Engineering. https://doi.org/10.1109/ICSE.2013.6606729

• [PS-02] Pavon, R., & Carpenter, B. (2013). Synthesis of Decision Making: From Data to Business Execution. https://doi.org/10.1109/ICDMW.2013.42

• [PS-03] Ivarsson, M., & Gorschek, T. (2012). Tool support for disseminating and improving development practices. Software Quality Journal. https://doi.org/10.1007/s11219-011-9139-6

• [PS-04] Leida, M., Majeed, B., Colombo, M., & Chu, A. (2013). LNBIP 162 - A Lightweight RDF Data Model for Business Process Analysis.

• [PS-05] Shibata, T., & Kurachi, Y. (2015). Big Data Analysis Solutions for Driving Innovation in On-site Decision Making, 51(2), 33–41.

• [PS-06] Roedder, N., Karaenke, P., Knapper, R., & Weinhardt, C. (2014). Decision-making based on incident data analysis. 16th IEEE Conference on Business Informatics, CBI 2014. https://doi.org/10.1109/CBI.2014.47

• [PS-07] Vera-Baquero, A., Colomo-Palacios, R., & Molloy, O. (2015). Real-time business activity monitoring and analysis of process performance on big-data domains. Telematics and Informatics. https://doi.org/10.1016/j.tele.2015.12.005

• [PS-08] Ogiela, L., & Ogiela, M. (2015). Semantic Data Analysis Algorithms Supporting Decision-making Processes. https://doi.org/10.1109/BWCCA.2015.108

• [PS-09] Esaki, K., Ichinose, Y., & Yamada, S. (2012). Statistical Analysis of Process Monitoring Data for Software Process Improvement and Its Application. American Journal of Operations Research, 2, 43–50. https://doi.org/10.4236/ajor.2012.21005

• [PS-10] Söylemez, M., & Tarhan, A. (2013). Using process enactment data analysis to support orthogonal defect classification for software process improvement. Joint Conference of the 23rd IWSM-MENSURA 2013. https://doi.org/10.1109/IWSM-Mensura.2013.27

• [PS-11] Fazzinga, B., Flesca, S., Furfaro, F., Masciari, E., & Pontieri, L. (2015). A compression-based framework for the efficient analysis of business process logs. https://doi.org/10.1145/2791347.2791351

• [PS-12] Begoli, E. (2012). A Short Survey on the State of the Art in Architectures and Platforms for Large Scale Data Analysis and Knowledge Discovery from Data.

• [PS-13] Serban, F. (2013). A Survey of Intelligent Assistants for Data Analysis. ACM Comput. Surv, 45(35). https://doi.org/10.1145/2480741.2480748

• [PS-14] Sureka, A., Kumar, A., & Gupta, S. (2015). Ahaan: Software Process Intelligence: Mining Software Process Data for Extracting Actionable Information. https://doi.org/10.1145/2723742.2723763

• [PS-15] Zheng, L., Zeng, C., Li, L., Jiang, Y., Xue, W., Li, J., Wang, P. (2014). Applying Data Mining Techniques to Address Critical Process Optimization Needs in Advanced Manufacturing. https://doi.org/10.1145/2623330.2623347

• [PS-16] Santos, T. A., Lima, A. M., Lima Reis, C. A., & Quites Reis, R. (2014). Automated Support for Human Resource Allocation in Software Process by Cluster Analysis. https://doi.org/10.1145/2593822.2593830

• [PS-17] Grabova, O., Darmont, J., Chauchat, J.-H., & Zolotaryova, I. (2010). Business Intelligence for Small and Middle-Sized Entreprises.

• [PS-18] Menzies, T., Kocaguneli, E., Peters, F., Turhan, B., & Minku, L. L. (2013). Data Science for Software Engineering.

• [PS-19] Bertini, E., & Lalanne, D. (2010). Investigating and Reflecting on the Integration of Automatic Data Analysis and Visualization in Knowledge Discovery.

• [PS-20] Houston, D. X., & Buettner, D. J. (2013). Modeling User Story Completion of an Agile Software Process.

• [PS-21] Mazón, J.-N., Zubcoff, J. J., Garrigós, I., Espinosa, R., & Rodríguez, R. (2012). Open Business Intelligence: on the importance of data quality awareness in user-friendly data mining.

• [PS-22] Rao, J. J., Kelappan, R., & Pallath, P. (2014). Recommendation System to Enhance Planning of Software Development using R. https://doi.org/10.1145/2593822.2593831

• [PS-23] Deline, R. (2015). Research Opportunities for the Big Data Era of Software Engineering. In Proceedings - 1st International Workshop on Big Data Software Engineering, BIGDSE 2015. https://doi.org/10.1109/BIGDSE.2015.13

• [PS-24] Zhang, D., Dang, Y., Lou, J.-G., Han, S., Zhang, H., & Xie, T. (2011). Software Analytics as a Learning Case in Practice: Approaches and Experiences.

• [PS-25] Marcus, A., & Menzies, T. (2010). Software is Data Too.