Statistical Control of Software Process: A Systematic Review

Main Article Content

Bibiana Y. Garcés
Francisco J. Pino

Abstract

The Software Processes Improvement requires advanced techniques for the quantitative management process, so that from the analysis of the relevant indicators in the organization, actions can be taken to facilitate the decision-making process in favor of this improvement. Among the set of techniques, special attention is devoted to the Statistical Process Control (SPC), which has gained acceptance in enterprises preparing to attain high degrees of maturity in their processes, and require the establishment of measuring programs. SPC is useful in software development for several reasons. Control Charts help identify process shifts and abnormal variations. It is therefore pertinent to assess how successful SPC is in the context of software production. In this sense, we have not found any integrated proposal of quantitative management applied to distributed software development, processes that allow the attainment of important benefits, and, at the same time, that more organizations are improving their processes. These considerations motivate the need to define and carry out a Systematic Review to assess whether SPC is being used effectively and correctly, and to determine the main obstacles to a successful application of SPC in SPI efforts. Thus, the systematic review allows developing a rigorous analysis of the current state of the SPC, which is a starting point to address how it can be successfully used as a decision-making support tool in software-process improvement in agreement with the available empirical evidence reported in the literature.

Article Details

Section
Reviews
Author Biographies

Bibiana Y. Garcés, Corporación Universitaria Comfacauca

Master in Informatics Engineering, Master in Advanced Computer Technologies from Universidad de Castilla La Mancha (Spain), and Engineering in Industrial Automation from the Universidad del Cauca (Colombia). She is currently Professor and Coordinator of Social Projection at the Corporación Universitaria de Comfacauca´s Engineering Program. She also serves as a consultant on implementation and improvement of software factories in Kybele Consulting Colombia SAS. His areas of research and professional interest are quality and statistical process control software for advanced global software process improvement.

Francisco J. Pino, Universidad del Cauca

Doctor in Informatics Engineering from Universidad de Castilla-La Mancha (Spain), Electronics and Telecommunications Engineer, and Specialist in Networks and Telematics Services from Universidad del Cauca (Colombia). He is a professor attached to the Faculty of Electrical Engineering and Telecommunications, member of IDIS Research Group (Research and Development in Software Engineering) at the Universidad del Cauca, and founder of Kybele Consulting Colombia SAS, an advisory company in quality and process, products, and services software improvement. Chief Auditor AENOR ISO 15504-SPICE. His research and professional interests are quality and process improvement of software development in small enterprises and multi-model environments.

References

Barbiero, C., Flury , M., Pagura, J., Quaglino, M., & Ruggieri , M. (2005). La importancia de la estadística en estrategias de mejora continua de la calidad. La metodología Seis Sigma. (Vol. Décimas Jornadas "Investigaciones en la Facultad"). Rosario, Argentina: Universidad Nacional de Rosario.

Hale, C., & Row, M. (2012). Do Not Get Out of Control: Achieving Real-time Quality and Performance. Retrieved from Crosstlak on line, http://www.crosstalkonline.org/storage/issue-archives/2012/201201/201201-Hale.pdf.

Park, Y., Choi, H., & Baik, J. (2007). A Framework for the Use of Six Sigma Tools in PSP/TSP. In Software Engineering Research, Management & Applications, 2007. SERA 2007. 5th ACIS International Conference on (pp. 807-814). Los Alamitos, CA: IEEE Computer Society.

Russ, R., Sperling , D., Rometsch, F., & Louis, P. (2008). Applying Six Sigma in the field of software engineering. In Software process and product measurement (pp. 36-47). Berlin Heidelberg, Germany: Spinger.

Wang, Q., Jiang, N., Gou, L., Liu, X., Li, M., & W, Y. (2006). BSR: A Statistic-Based Approach for Establishing. Proceedings of the 28th international conference on Software engineering (pp. 585-594). New York, NY: ACM.

Xiaosong, Z., Zhen, H., Fangfang , G., & Shenqi, Z. (2008). Research on the Application of Six Sigma in Software Process Improvement. Intelligent Information Hiding and Multimedia Signal Processing, 2008. IIHMSP'08 International Conference (pp. 937-940). Piscataway, NJ: IEEE.

Alzate Naranjo, J., & Molina Correa, A. (2009). Gestión cuantitativa del proceso de desarrollo de software . Medellín, Colombia: Universidad EAFIT.

Baldasarre, M. T., Caivano, D., Kitchenham, B., & Visaggio, G. (2007). Systematic Review of Statical Process Control: An Experience Report. 11th International Conference on Evaluation and Assessment in Software Engineering (EASE). Keele, UK: Keele University.

Baldassarre, M. T., Boffoli, N., Caivano, D., & Visaggio, G. (2008). A hands on approach for Teaching Systematic Review. In Product-focused software process improvement (pp. 415-426). Berlin Heidelberg, Germany: Springer.

Baldassarre, M., Caivano, D., & Visaggio, G. (2006). Non Invasive Monitoring of a Distributed Maintenance Process. Instrumentation and Measurement Technology Conference, 2006. IMTC 2006. Proceedings of the IEEE (pp. 1098-1103). Piscataway, NJ: IEEE.

Baldassarre, T., Boffoli, N., Caivano, D., & Visaggio, G. (2004). Manging software process improvement (SPI) through statical rocess control (SPC). In Product Focused Software Process Improvement (pp. 30-46). Berlin Heidelberg, Germany: Springer.

Batini, C., & Sacannapieco, M. (2006). Data quality: concepts, methodologies and techniques. Berlin, Germany: Springer Verlag.

Boffoli, N., Bruno, G., Caivano, D., & Mastelloni, G. (2008). Statistical process control for software: a systematic approach. Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (pp. 327-329). New York, NY: ACM.

Brereton , P., Kitchenham , B., Budgen, D., Turner, M., & Khalil, M. (2005). Employing sistematic literature review: An experience report [unpublished draft]. Keele, UK: School of Computing & Mathematics, Keele University.

Burr, A. & Owen, M. (1996). Statical methods for software quality. Scottsdale, AZ: Coriolis

Caballero , I. (2008). Introducción a la calidad de los datos y de la información. Escuela Superior de Informatica. Ciudad Real, Spain: Universidad Castilla la Mancha.

Caivano, D. (2000). Statical process control. (SERLAB, Ed.) Bari, Italy: Università degli Studi di Bari.

Caivano, D. (2011). Six Sigma for Software [Case Study]. II seminario si incentrerà in maniera pratica. Italy.

Card , D., Domzalski, K., & Davies, G. (2008). Making statistics part of decision making in an engineering organization. IEEE Software, 25(3), 37-47.

Card, D. (1994). Statical process control for software. IEEE SOftware, 11(3), 95-97.

Carleton, A., & Florac, A. (1999). Statically Controlling the Software Process. The 99 SEI Software Engineering Symposium. Pittsburgh, PA: Software Engineering Institute, Carnegie Mellon University.

Ferreiro, O. P. (2010). Control estadístico de procesos y estrategias Seis Sigma. Fonte: http://www.oocities.org/es/foro2_control_de_procesos/genlace1.pdf

Florac, W. A., & Carleton, A. D. (1999). Measuring the software process: statical process control for software process improvement. Indianapolis, IN: Pearson.

Gardiner, J., & Montgomery, D. (1987). Using statistical control of charts for software quality control. Quality and Reliability Engineering International, 3, 15-20.

Genero, M., Fernández, A. M., Nelson, H. J., & Piattini, M. (2011). How to perform systematic reviews: Theory and examples. In A. R. group (Ed.), Metodologías y Técnicas de Investigación en Informática. Universidad Castilla - La Mancha: Ciudad Real, Spain.

ISO. (2003). Guidance on statistical techniques for ISO 9001:2000. Geneva, Switzerland: ISO.

Juran, J. M. (1951). Juran´s quality control handbook. New York, NY: McGraw Hill.

Kasunic, M., McCurley, J., Goldenson, D., & Zubrow, D. (2011). An investigation of techniques for detecting data anomalies in earned value management data [tech. Rep-]. Pittsburgh, PA: Carnegie Mellon University.

Kitchenham, B. (2004). Procedures for performing systematic reviews [technical report TR/SE0401]. Keele, UK: Keele University.

Kitchenham, B. (2007). Guidelines for performing systematic literature reviews in softwrare engineering [EBSE technical report]. (D. o.-U. Sotfware Engineering Group - Keele University) Keele, UK: Keele University.

Komuro, M. (2006). Experiences of Applying SPC Techniques to software Development Processes. Proceedings of the 28th international conference on Software engineering (pp. 577-584). New York, NY: ACM.

Lantzy, M. (1992). Application of Statical Process Control to the Software Process. Proceeding WADAS '92 Proceedings of the ninth Washington Ada symposium on Ada: Empowering software users and developers (pp. 113-119). New York, NY: ACM.

Lefcovich, M. L. (2004). Six Sigma, un nuevo paradigma en gestión. Buenos Aires, Argentina: El Cid.

López. (2007). Six Sigma aplicado a la gestión de Mantenimiento en la empresa Drummond Ltd [monografía]. Universidad Industrial de Santander: Bucaramanga, Colombia.

Manlove, D., & Kan, S. (2007). Practical statistical process control for software metrics. Software Quality Professional Magazine, 9(4), 15-26.

Molina C., A. M., Alzate N., J. A., & Rincon B., R. D. (2009). Gestión Cuantitativa del proceso de desarrollo de software [thesis]. Escuela de Ingeniería de Sistemas. Medellin, Colombia: Universidad EAFITs.

Montoni, M., Rocha, A., & Weber, K. C. (2009). MPS. BR: A succesful program for softwrae process improvement in Brazil. Journal of Software Maintenance and Evolution. Research and Practice , 14(5), 289-300.

Muro, G. (2007). Aplicación de la metodología "Six Sigma" (6 o) a un mantenimiento preventivo de una turbina eólica [degree paper]. Universidad Pública de Navarra: Pamplona, Spain.

Paulk, M. C., Weber, C. V., Garcia, S. M., Chrissis, M., & Bush, M. (1993). Key practices of capability maturity model [Tech. Rep.] (Vol. Version 1.1). (S. E. Institute, Ed.) Pittsburgh, PA: Carnegie Mellon University.

Perini, M. B., Rocha, A. R., & Falbo, R. D. (2010). Evaluating the Suitability of a Measurement Repository for Statical Process Control. In ESEM´10 (Ed.), Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (p. 27). New York, NY: IEEE.

Piattini V., M. G., García Rubio, F. Ó., Garzás Parra, J., & Genero Bocco, M. F. (2008). Medición y estimación del software. Madrid, Spain: Ra-Ma.

Pino, F. J., García, F., & Piattini, M. (2007). Software process improvement in small and medium software enterprises: a systematic review. Software Quality Journal, 16(2), 237-261.

Ruiz-Falcó, A. (2006). Control estadístico de procesos [class notes]. Madrid, Spain: Universidad Pontificia Comillas.

Salin Monteiro, L. F., & Marcal de Oliveira, K. (2009). Defining a catalog of indicators to support process. Journal of Software Maintenance and Evolution: Research and Practice, 23(6), 395-422.

Sargut, K., & Deminörs, O. (2006). Utilization of statical process control (SPC) in emergent software organizations: pitfalls and suggestions. Software Quality Journal, 14(2), 135-157.

Tarhan , A., & Demirörs, O. (2008). Assessment of software process and metrics. Lecture Notes in Computer Science, 4895, 102-113.

Tarhan, A., & Demirors, O. (2006). Investigating suitability of software process and metric for statical process control. In Software Process Improvement (pp. 88-99). Berlin Heidelberg, Germany: Springer.

Tayntor, C. (2007). Six Sigma software development (Vol. 2). Boca Raton, FL: Auerbach.

Wang , Q., Gou, L., Jiang, N., & Che, M. (2007). An empirical study on establishing quantitative. Lecture Notes in Computer Science, 4470, 233-245.

Wang, Q., Jiang, N., Gou, L., Liu, X., Li, M., & Wang, Y. (2006). BSR: A Statistic-based Approach for Establishing and Refining Software Process Performance Baseline. ICSE '06 Proceedings of the 28th international conference on Software engineering (pp. 585-594). New York, NY: ACM.

Weller, E. (2000). Practical applications of statistical process control. IEEE Software, 17(3), 48-55.