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Relative risk proneness in phases of software development: metric based approach with Cox model

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Abstract

The software suffers from confounding effect due to defects that occurs during its entire development process. Software failure occurs due to various reasons. One of the reasons can be removal of defects at a much later stage, even though it has been detected in early phases of software development. Defect prediction has emerged as an interesting area for researchers within stipulated time period. Prediction depends mainly on the modeling of these defects and while modeling the simplest parameter used by researchers is the software size. In this paper, we showed deployment of Cox model and investigated the significance on defect prediction during various phases of development. The parameter used here is the defect count in various phases. Next, we proposed and compared two strategies for effective overall risk prediction of the projects using another proposed metric “Relative Risk Proneness Probability”. This metric is used in phases as evaluation criteria for judging the cost effectiveness of the project.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, BIT Mesra, Ranchi, Jharkhand, 835215, India

    Pooja Jha & K. Sridhar Patnaik

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  1. Pooja Jha
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  2. K. Sridhar Patnaik
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Correspondence to K. Sridhar Patnaik.

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Appendix: Derivation of the equation for relative risk proneness probability (RRPP)

Appendix: Derivation of the equation for relative risk proneness probability (RRPP)

In this appendix, we explained the calculation of RRPP of projects chosen by first strategy with respect to another strategy. The first strategy considers first three phases in which the defects recorded are assumed to be d1, d2, d3, d4 and d5. The second strategy considers two phases having detected defects as D1, D2, D3, D4 and D5.

First, let us take a project as reference with total defects P. As we have identified the link function to be logarithmic in nature, we used the Eq. (4) without time parameter t to show the RRPP of individual project as:

$$ e^{{({\text{In}}\,d = {\text{In}}\,P)}} = e^{{\beta {\text{In}}\frac{d}{P}}} $$

For every strategy, we calculated the sum of RRPP of selected individual phases with respect to reference project P. To find the RRPP, we took the ratio of their sums as:

$$ \begin{aligned} RRPP & = \frac{{\sum\nolimits_{i = 1}^{i = n} {e^{{\beta ({\text{In}}\,d_{i} = {\text{In}}\,P)}} } }}{{\sum\nolimits_{i = 1}^{i = n} {e^{{\beta ({\text{In}}\,D_{i} = {\text{In}}\,P)}} } }} \\ & = \frac{{\sum\nolimits_{i = 1}^{i = n} {\frac{{e^{{\beta {\text{In}}\,d_{i} }} }}{{e^{{\beta {\text{In}}\,P}} }}} }}{{\sum\nolimits_{i = 1}^{i = n} {\frac{{e^{{\beta {\text{In}}\,D_{i} }} }}{{e^{{\beta {\text{In}}\,P}} }}} }} \\ & = \frac{{\frac{1}{{e^{{\beta {\text{In}}\,P}} }}\sum\nolimits_{i = 1}^{i = n} {e^{{\beta {\text{In}}\,d_{i} }} } }}{{\frac{1}{{e^{{\beta {\text{In}}\,P}} }}\sum\nolimits_{i = 1}^{i = n} {e^{{\beta {\text{In}}\,D_{i} }} } }} \\ & = \frac{{\sum\nolimits_{i = 1}^{i = n} {e^{{\beta \,{\text{In}}\,d_{i} }} } }}{{\sum\nolimits_{i = 1}^{i = n} {e^{{\beta \,{\text{In}}\,D_{i} }} } }} \\ \end{aligned} $$

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Jha, P., Patnaik, K.S. Relative risk proneness in phases of software development: metric based approach with Cox model. Int J Syst Assur Eng Manag 10, 1544–1554 (2019). https://doi.org/10.1007/s13198-019-00904-8

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  • DOI: https://doi.org/10.1007/s13198-019-00904-8

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