An Empirical Evaluation of Assorted Risk Management Models and Frameworks in Software Development

An Empirical Evaluation of Assorted Risk Management Models and Frameworks in Software Development

Alankrita Aggarwal (IKG Punjab Technical University, Jallandhar Punjab, India), Kanwalvir Singh Dhindsa (Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib Punjab, India) and P. K. Suri (Kurukshetra University, Kurukshetra, India)
Copyright: © 2020 |Pages: 11
DOI: 10.4018/IJAEC.2020010104

Abstract

Software risk management is one the key factors in software project management with the goal to improve quality as avoid vulnerabilities. The term defect refers to an imperfection that may arise because of reasons including programmers' skills, lack of suitable testing strategies, and many others. When actual results are different from expected result or meeting wrong requirement, it is called defect and it forms the basis of risk escalation in a software project which is obviously not accepted in any type of deployment. Making a reliable software should be risk free from any vulnerability. Along with reliability another issue arises is software quality which is a factor with software risk management. The quality of software is to reduce the occurrence of risks and defects with the objective to produce an effectual value software which is key point of consideration. In this article, is underlined the present assorted risk management strategies proposed and projected by a number of researchers and academicians on the different parameters using benchmark datasets from renowned sources of research.
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2. Prominent Approaches For Risk And Defects Management In Software Development

To produce high quality software the prediction of defects is very important. Following techniques are used for prediction:

Machine Learning

Software risk management and defects prediction is very popular in software engineering as it helps to reduce the cost by predicting the defect at early phases. During the development of software for organizations defect prediction uses different Machine Learning Techniques (MLT). To make software reliable, the software should be defect free (Challagulla, Bastani, Yen, & Paul, 2005). The main goal of machine learning (ML) is to build up the algorithm of practical value and algorithms should be well-organized. These are used to build defect prediction model. In ML we not only deal with time and space but also with amount of data, so it is a data driven technique. Data plays a vital role here; data helps to determine that given set of data is used as training or testing purpose. Quality of software significantly improves using MLT in SDP. For improving quality, a large number of different flavours of machine techniques exist but no particular technique is better than other in term of SDP. Figure 1 shows a learning diagram.

Figure 1.

Learning diagram

IJAEC.2020010104.f01

Figure is depicting the Learning Diagram whereby the training and prediction process is presented effectually so that the further classification can be done with higher degree of performance and accuracy.

Advantages of Machine Learning

  • A general-purpose algorithm is generated for ML that produces more accurate result.

  • A large amount of data has been examined by MLT.

  • ML is the hope that it will provide insight into the general phenomenon of learning.

  • MLT is a data-driven technique hence you know the effect of the parameter and how they are used.

  • You will know how all of the parameters are used, their effects and even have insight into how it could be further parameterized to specialize it for a problem.

Disadvantages Machine Learning

  • Limited: ML will not work for every case. Number of times ML will fail therefore it requires some knowledge of the problem so that right algorithm should be applied.

  • Large data requirements: deep learning algorithm is used because some ML require large amount of data. The collection of a large amount of data and work with that data might leads to burdensome.

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