Multi-response optimization based on desirability function and Taguchi method in agile software development

Springer Science and Business Media LLC - Tập 10 - Trang 1444-1452 - 2019
Anusha Pai1, Gopalkrishna Joshi2, Suraj Rane3
1Department of Computer Engineering, Padre Conceicao College of Engineering, Verna, India
2Computer Science Department, KLE Technological University, Hubballi, India
3Mechanical Engineering Department, Goa College of Engineering, Farmagudi, Ponda, India

Tóm tắt

Software industries are continuously striving to achieve software quality through delivery of defect free software within time and within estimated budget. Most of the quality improvement strategies in software development focusses on optimizing a single response variable such as the defect count. But in reality, many responses contribute to the quality of software developed which requires to be studied simultaneously. This paper attempts to study the number of defects in software and the effort required in correcting these defects, simultaneously using desirability function and Taguchi method. Effort is defined as the time span from when the defect was logged into the system to the time when the defect was corrected and removed from the system and is measured in hours. The work has been executed on the issue tracking system of a large telecommunication organization. The organization manufactured consumer electronic devices having embedded software developed using agile development method. The input factors studied are Change Request Priority, software development phase and severity, which were selected from a total of 24 parameters using analytic hierarchy process. Significant factors and their optimal levels which optimized the defects captured and the effort taken to eliminate them, were obtained. Confirmation tests were performed using the optimal settings of the input factors on the data from the subsequent sprint which showed a 20% improvement in the overall desirability value.

Tài liệu tham khảo

Aich U, Banerjee S (2014) Modelling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization. Appl Math Model 38:2800–2818 Azzeh M, Nassif AB (2016) A hybrid model for estimating software project effort from use case points. Appl Soft Comput 49:981–989 Brasil MMA, da Silva TGN, de Freitas FG, de Souza JT, Cortes MI (2012) A multiobjective optimization approach to the software release planning with undefined number of releases and interdependent requirements. In: Enterprise information systems. Springer, Berlin Heidelberg, pp 300–314 Chatzipetrou P, Papatheocharous E, Angelis L, Andreou AS (2015) A multivariate statistical framework for the analysis of software effort phase distribution. Inf Softw Technol 59:149–169 Chen X, Shen Y, Cui Z, Ju X (2017) Applying feature selection to software defect prediction using multi-objective optimization. In: 2017 IEEE 41st annual computer software and applications conference (COMPSAC). vol 2, pp 54–59. https://doi.org/10.1109/compsac.2017.65 Derringer G, Suich R (1980) Simultaneous optimization of several response variables. J Qual Technol 12:214–219 Dubey AK, Yadava V (2008) Multi-objective optimization of Nd:YAG lase cutting of nickel-based superalloy sheet using orthogonal array with principal component analysis. Opt Lasers Eng 46:124–132 Elsayed K, Lacor C (2013) CFD modeling and multi-objective optimization of cyclone geometry using desirability function, artificial neural networks and genetic algorithms. Appl Math Model 37:5680–5704 Hazir E, Erdinler ES, Koc KH (2018) Optimization of CNC cutting parameters using design of experiment (DOE) and desirability function. J For Res 29:1423–1434 Hsu C-M (2012) Improving the lighting performance of a 3535 packaged hi-power LED using Genetic Programming, quality loss functions and particle swarm optimization. Appl Soft Comput 12:2933–2947 Jacobs J, Moll JV, Kusters R, Trienekens J, Brombacher A (2007) Identification of factors that influence defect injection and detection in development of software intensive products. Inf Softw Technol 49:774–789 Ju S, Shenoi RA, Jiang D, Sobey AJ (2013) Multi-parameter optimization of lightweight composite triangular truss structure based on response surface methodology. Compos Struct 97:107–116 Kumaresh S, Baskaran R (2012) Experimental design on defect analysis in software process improvement. In: International conference on recent advances in computing and software systems. pp 293–298. https://doi.org/10.1109/racss.2012.6212683 Lazic L, Milinkovic S (2015) Reducing software defects removal cost via design of experiments using Taguchi approach. Softw Qual J 23:267–295 Li X, Xie M, Huing S (2012) Multi-objective optimization approaches to software release time determination. Asia-Pac J Oper Res 29:1–19 López-Martín C (2015) Predictive accuracy comparison between neural networks and statistical regression for development effort of software projects. Appl Soft Comput 27:434–449 Mäntylä MV, Itkonen J (2014) How are software defects found? The role of implicit defect detection, individual responsibility, documents, and knowledge. Inf Softw Technol 56:1597–1612 Mueller C (2014) Multi-objective optimization of software architectures using ant colony optimization. In: Lecture notes on software engineering. vol 2, pp 371–374 Öztürk MM (2017) Which type of metrics are useful to deal with class imbalance in software defect prediction. Inf Softw Technol 92:17–29 Pandey RK, Panda SS (2015) Optimization of bone drilling using Taguchi methodology coupled with fuzzy based desirability function approach. J Intell Manuf 26:1121–1129 Phadke MS (2008) Quality engineering using robust design. Prentice-Hall, New Jersey Prasanna J, Karunamoorthy L, Raman MV, Prashanth S, Chordia R (2014) Optimization of process parameters of small hole dry drilling in Ti–6A1–4V using Taguchi and Grey relational analysis. Measurement 48:346–354 Ross PJ (2005) Taguchi techniques for quality engineering, 2nd edn. McGraw Hill, New York Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York Sommerville I (2017) Software engineering, 10th edn. Pearson India, Noida Tad G, Kiyoshi I (2010) Multi-objective optimization for software development projects. In: Lecture notes in engineering and computer science. p 2180 Zhou J, Wang B, Lin J, Fu L (2013) Optimization of an aluminium alloy anti-collision side beam hot stamping process using a multi-objective genetic algorithm. Arch Civ Mech Eng 13:401–411