Design for Software Risk Management Using Soft Computing and Simulated Biological Approach

Design for Software Risk Management Using Soft Computing and Simulated Biological Approach

Alankrita Aggarwal (Department of Computer Science and Engineering, IKG Punjab Technical University, Jallandhar, India), Kanwalvir S. Dhindsa (Department of Computer Science and Engineering, Baba Banda Singh Bahadur Engineering College, Fatehgarh, India) and P. K. Suri (Kurukshetra University, Kurukshetra, India)
DOI: 10.4018/IJSPPC.2020040104
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Abstract

The process of software evaluation is one of the key tasks that are addressed by the quality assurance teams so that the risks in the software suite can be identified and can be removed with prior notifications. Different types of metrics can be used in defect prediction models, and widely used metrics are source code and process metrics. A simulated environment for the entire process shall be generated for multiple scenarios and parameters so that the results and conclusion can be depicted in an effective way. The focus of research is to develop a narrative architecture and design for software risk management using soft computing and nature-inspired approach. The proposed approach titled simulated biological reaction (SBR) is expected to have the effectual results on multiple parameters with the flavor of soft computing-based optimization. The proposed approach shall be integrating the simulation of microbiological process in different substances and elements to produce a new substance.
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1. Introduction

The process of software defects based engineering and associated prediction is one of the key tasks while developing the effectual software products. Software Defect Prediction (Lyytinen et al. 2016; Gao et al., 2011; Turhan et al., 2007) in software engineering used to predict the deformity in the software module. Numbers of defect are present during the development or after the delivery of software module. To obtain high quality software the prediction process is followed to predict to the defects. The overall performance and defect free software create huge loyalty and faith of the client. Few big organizations are using this prediction process as they release their software and software versions frequently and they have less time so instead of manually predicting the defects they use software analysis process.

The work on the nature inspired approaches are not quite new and widely variety of algorithms are available even for other engineering domains. Using such approaches the higher degree of accuracy can be achieved with the greater values in the outcomes. In this research work, the presentation of the nature inspired approaches towards the specific scenarios of software quality are depicted and found that the greater accuracy can be achieved using such approaches.

Following Table 1 shows there is a relative strength and weaknesses of four risk management approaches. The following Table 1 is presenting the risk management approaches and the criteria towards the performance issues like what are the listing criteria, making a list of risk against actions, any strategic modeling and analysis of the strategies made.

Table 1.
Evaluation of prominent strategies (Lyytinen et al. 2016; Gao et al., 2011)
ParadigmUsageBuilding StrategiesModificationsAdaptabilityResolutions Management
Listing CriteriaYesYesYesYesNo
List towards ActionsYesYesYesYesYes
Strategic ModelingYesNoNoYesYes
Strategic
Analytics
NoNoYesYesYes

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