Software Productivity Measurement and Prediction Methods: what can we tell about them?
Resumo
An adequate way of making software organizations to remain competitive is to ensure their innovative capacity and the continuous increasing of their software process productivity with quality. Indeed, the ability on increasing the software productivity relies on among other issues in the organization's measurement and prediction capacity. Productivity refers to the rate at which a company produces goods, and its observation takes into account the number of people and the amount of other necessary resources to produce such goods. However, it is not clear how productivity can be observed when the product is software. Therefore, this work presents the results of an investigation regarding software productivity measurement and prediction methods. A previous systematic literature review was evolved and re-executed, limited to the year 2013. It allowed the identification of 89 new primary studies evidencing that: (1) ratio-based and weighted factors analyses still represent most of the methods applied to measure, describe and interpret software productivity; (2) 24 factors present evidence of influencing productivity, and; (3) SLOC-based measures, despite the criticism and issues associated with these sort of measurements, are the most common measures used in the studies.
Palavras-chave:
Software, Prediction, Methods
Referências
Ardagna C, Damiani E, Frati F, Oltolina S, Regoli M and Ruffatti G, 2010, Spago4Q and the QEST nD Model: An Open Source Solution for Software Performance Measurement, IFIP AICT, Vol. 319, pp. 1-14.
de Aquino Junior, G S de; Meira, S, 2009a, Software Productivity Measurement: Past Analysis And Future Trends, In: Proc. 3th ICSETP.
de Aquino Junior GS and de Lemos Meira SR, 2009b, An Approach to Measure ValueBased Productivity in Software Projects, In: Proc of 9th QSIC., pp. 383-389.
Boehm B, 2003, Value-Based Software Engineering: Reinventing, SIGSOFT Softw. Eng. Notes, 28(2):3.
Bibi S, Stamelos I and Angelis L, 2008, Combining Probabilistic Models for Explanatory Productivity Estimation, Elsevier IST, pp. 656-669.
Biolchini, J, Mian, P G, Natali, A C, Travassos, G H, 2005, Systematic Review in Software Engineering: Relevance and Utility. Technical Report. PESC COPPE/UFRJ. Brazil. http://www.cos.ufrj.br/uploadfiles/es67905.pdf
Celik, N.; Lee, S.; Mazhari, E.; Son, Y.-J.; Lemaire, R. and Provan, K. G. (2011), Simulation-based workforce assignment in a multi-organizational social network for alliance-based software development, SMPT 19(10), 2169 2188.
Chapetta,WA, 2016a, Supplementary Material of SLR-2014, https://goo.gl/1eGNme , Accessed in Jun 30, 2016.
Chapetta,WA, 2016b, Evidence (selected articles) vs Factors, https://goo.gl/zKC6PP , Accessed in Jun 30, 2016.
Dale, H. van der Zee, 1992, Software Productivity Metrics: Who Needs Them?, Elsevier IST, pp. 731–738.
Dybå T, Dingsøyr T, Hanssen GK, 2007, Applying Systematic Reviews to Diverse Study Types: An Experience Report, In: Proc. of the 1st ESEM 2007, pp. 225–234.
Hernandez-López, A.; Colomo-Palacios, R.; Garca-Crespo, A., Cabezas-Isla, F., 2011, Software Engineering Productivity: Concepts, Issues, and Challenges, IGI Global, Vol. 2, pp. 37-47
Hernandez-Lopez, A.; Colomo-Palacios, R.; Garcia-Crespo, A., 2013, Software Engineering Job Productivity A Systematic Review, IJSEKE, Vol. 23, pp. 387-406.
Kang D, Jung J and Bae D-H, 2011, Constraint-based human resource allocation in software projects, Software-Practice and Experience, Vol. 41(5), pp. 551-577.
Hwang S-M and Kim H-M, 2005, A Study on Metrics for Supporting The Software Process Improvement Based on SPICE, LNCS,Vol. 3647, pp. 71-80.
Lopez-Martin, C.; Chavoya, A. and Meda-CAMPANA, M. E. (2013), Software development productivity prediction of individual projects applying a neural network, In: Proc. of 6th IMETI, pp. 47-52.
Monteiro L and de Oliveira K, 2011, Defining a Catalog of Indicators to Support Process Performance Analysis, Wiley JSME. Vol. 23(6), pp. 395-422.
Pai, M., McCulloch, M., Gorman, J. D., Pai, N., Enanoria, W., Kennedy, G. & Colford Jr, J. M. , 2004, Systematic Reviews and meta-analyses: An illustrated, step-by-step guide, The National Medical Journal of India, vol. 17, n.2.
Petersen K, 2011, Measuring and Predicting Software Productivity: A Systematic Map And Review, Elsevier IST, Vol. 53, pp. 317-343.
Putnam LH and Myers W, 1991, Measures for Excellence: Reliable Software on Time, within Budget. Prentice Hall Professional Technical Reference.
Ramil J and Lehman M (2001), Defining and applying metrics in the context of continuing software evolution, In: Proc. of 7th METRICS 2001, pp. 199-209.
Scacchi W, 1991, Understanding Software Productivity: A Knowledge-Based Approach, World Scientific IJSEKE, pp. 293–321.
Santos G, Kalinowski M, Rocha A, Travassos G, Weber K and Antonioni J, 2010, MPS.BR: A Tale of Software Process Improvement and Performance Results in the Brazilian Software Industry, In: Proc. of 7th QUATIC, 2010, pp. 412-417.
de Aquino Junior, G S de; Meira, S, 2009a, Software Productivity Measurement: Past Analysis And Future Trends, In: Proc. 3th ICSETP.
de Aquino Junior GS and de Lemos Meira SR, 2009b, An Approach to Measure ValueBased Productivity in Software Projects, In: Proc of 9th QSIC., pp. 383-389.
Boehm B, 2003, Value-Based Software Engineering: Reinventing, SIGSOFT Softw. Eng. Notes, 28(2):3.
Bibi S, Stamelos I and Angelis L, 2008, Combining Probabilistic Models for Explanatory Productivity Estimation, Elsevier IST, pp. 656-669.
Biolchini, J, Mian, P G, Natali, A C, Travassos, G H, 2005, Systematic Review in Software Engineering: Relevance and Utility. Technical Report. PESC COPPE/UFRJ. Brazil. http://www.cos.ufrj.br/uploadfiles/es67905.pdf
Celik, N.; Lee, S.; Mazhari, E.; Son, Y.-J.; Lemaire, R. and Provan, K. G. (2011), Simulation-based workforce assignment in a multi-organizational social network for alliance-based software development, SMPT 19(10), 2169 2188.
Chapetta,WA, 2016a, Supplementary Material of SLR-2014, https://goo.gl/1eGNme , Accessed in Jun 30, 2016.
Chapetta,WA, 2016b, Evidence (selected articles) vs Factors, https://goo.gl/zKC6PP , Accessed in Jun 30, 2016.
Dale, H. van der Zee, 1992, Software Productivity Metrics: Who Needs Them?, Elsevier IST, pp. 731–738.
Dybå T, Dingsøyr T, Hanssen GK, 2007, Applying Systematic Reviews to Diverse Study Types: An Experience Report, In: Proc. of the 1st ESEM 2007, pp. 225–234.
Hernandez-López, A.; Colomo-Palacios, R.; Garca-Crespo, A., Cabezas-Isla, F., 2011, Software Engineering Productivity: Concepts, Issues, and Challenges, IGI Global, Vol. 2, pp. 37-47
Hernandez-Lopez, A.; Colomo-Palacios, R.; Garcia-Crespo, A., 2013, Software Engineering Job Productivity A Systematic Review, IJSEKE, Vol. 23, pp. 387-406.
Kang D, Jung J and Bae D-H, 2011, Constraint-based human resource allocation in software projects, Software-Practice and Experience, Vol. 41(5), pp. 551-577.
Hwang S-M and Kim H-M, 2005, A Study on Metrics for Supporting The Software Process Improvement Based on SPICE, LNCS,Vol. 3647, pp. 71-80.
Lopez-Martin, C.; Chavoya, A. and Meda-CAMPANA, M. E. (2013), Software development productivity prediction of individual projects applying a neural network, In: Proc. of 6th IMETI, pp. 47-52.
Monteiro L and de Oliveira K, 2011, Defining a Catalog of Indicators to Support Process Performance Analysis, Wiley JSME. Vol. 23(6), pp. 395-422.
Pai, M., McCulloch, M., Gorman, J. D., Pai, N., Enanoria, W., Kennedy, G. & Colford Jr, J. M. , 2004, Systematic Reviews and meta-analyses: An illustrated, step-by-step guide, The National Medical Journal of India, vol. 17, n.2.
Petersen K, 2011, Measuring and Predicting Software Productivity: A Systematic Map And Review, Elsevier IST, Vol. 53, pp. 317-343.
Putnam LH and Myers W, 1991, Measures for Excellence: Reliable Software on Time, within Budget. Prentice Hall Professional Technical Reference.
Ramil J and Lehman M (2001), Defining and applying metrics in the context of continuing software evolution, In: Proc. of 7th METRICS 2001, pp. 199-209.
Scacchi W, 1991, Understanding Software Productivity: A Knowledge-Based Approach, World Scientific IJSEKE, pp. 293–321.
Santos G, Kalinowski M, Rocha A, Travassos G, Weber K and Antonioni J, 2010, MPS.BR: A Tale of Software Process Improvement and Performance Results in the Brazilian Software Industry, In: Proc. of 7th QUATIC, 2010, pp. 412-417.
Publicado
24/10/2016
Como Citar
CHAPETTA, Wladmir Araujo; TRAVASSOS, Guilherme Horta.
Software Productivity Measurement and Prediction Methods: what can we tell about them?. In: SIMPÓSIO BRASILEIRO DE QUALIDADE DE SOFTWARE (SBQS), 15. , 2016, Maceió.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 211-225.
DOI: https://doi.org/10.5753/sbqs.2016.15136.
