Why interaction between metrics should be considered in the development of software quality models: a preliminary study

This study examines the need to consider interactions between the measurements (metrics) of different quality factors in the development of software quality models. Though the correlation between metrics has been explored to a considerable depth in the development of these models, consideration of interactions between predictors is comparatively new in software engineering. This preliminary study is supported by statistically-proven results, differentiating interactions with correlation analysis. The issues raised here will assist analysts to improve empirical analyses by incorporating interactions in software quality model development, where amalgamating effects between different characteristics or subcharacteristics are observed.

[1]  Elmar Jürgens,et al.  Software quality models: Purposes, usage scenarios and requirements , 2009, 2009 ICSE Workshop on Software Quality.

[2]  Yogesh Singh,et al.  Suitability of KNN Regression in the Development of Interaction based Software Fault Prediction Models , 2014 .

[3]  L. Hayduk,et al.  Latent Variable Interaction and Quadratic Effect Estimation: A Two-Step Technique Using Structural Equation Analysis , 1996 .

[4]  Helfried Moosbrugger,et al.  Challenges in Nonlinear Structural Equation Modeling , 2007 .

[5]  Beata Czarnacka-Chrobot.  The ISO/IEC Standards for the Software Processes and Products Measurement , 2009, SoMeT.

[6]  Stefan Wagner,et al.  A Bayesian network approach to assess and predict software quality using activity-based quality models , 2009, PROMISE '09.

[7]  Ho-Won Jung,et al.  Measuring software product quality: a survey of ISO/IEC 9126 , 2004, IEEE Software.

[8]  Tibor Gyimóthy,et al.  Empirical validation of object-oriented metrics on open source software for fault prediction , 2005, IEEE Transactions on Software Engineering.

[9]  D. A. Kenny,et al.  The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. , 1986, Journal of personality and social psychology.

[10]  Oliver Linton,et al.  Testing additivity in generalized nonparametric regression models with estimated parameters , 2001 .

[11]  Lionel C. Briand,et al.  Dynamic coupling measurement for object-oriented software , 2004, IEEE Transactions on Software Engineering.

[12]  Yogesh Singh,et al.  Impact of Interaction in the Combined Metrics Approach for Fault Prediction , 2013 .

[13]  Norman E. Fenton,et al.  A Critique of Software Defect Prediction Models , 1999, IEEE Trans. Software Eng..

[14]  Fan Yang,et al.  Nonlinear structural equation models: The Kenny-Judd model with Interaction effects , 1996 .

[15]  Yogesh Singh,et al.  Comparison of M5’ Model Tree with MLR in the Development of Fault Prediction Models Involving Interaction Between Metrics , 2015 .

[16]  R. Friedrich.  In Defense of Multiplicative Terms In Multiple Regression Equations , 1982 .

[17]  D. A. Kenny,et al.  Estimating the nonlinear and interactive effects of latent variables. , 1984 .

[18]  Tracy Hall,et al.  Developing Fault-Prediction Models: What the Research Can Show Industry , 2011, IEEE Software.

[19]  Kurt D. Welker,et al.  Software Maintainability Index Revisited , 2001 .

[20]  Jacob Cohen,et al.  Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .

[21]  Burak Turhan,et al.  Better, faster, and cheaper: what is better software? , 2010, PROMISE '10.

[22]  David Afshartous,et al.  Key Results of Interaction Models with Centering , 2011 .

[23]  Yogesh Singh,et al.  Identifying influential metrics in the combined metrics approach of fault prediction , 2013, SpringerPlus.

[24]  Taghi M. Khoshgoftaar,et al.  An empirical study of predicting software faults with case-based reasoning , 2006, Software Quality Journal.

[25]  Catherine Stringfellow,et al.  An Empirical Method for Selecting Software Reliability Growth Models , 2002, Empirical Software Engineering.