scholarly journals A Framework for Software Risk Management

1996 ◽  
Vol 11 (4) ◽  
pp. 275-285 ◽  
Author(s):  
Kalle Lyytinen ◽  
Lars Mathiassen ◽  
Janne Ropponen
Keyword(s):  
Risk Management ◽  
Software Development ◽  
Empirical Analysis ◽  
Practical Tool ◽  
Software Risk Management ◽  
Software Risk ◽  
Management Approaches ◽  
Risk Managers ◽  
Analytical Device

We present a simple, but powerful framework for software risk management. The framework synthesizes, refines, and extends current approaches to managing software risks. We illustrate its usefulness through an empirical analysis of two software development episodes involving high risks. The framework can be used as an analytical device to evaluate and improve risk management approaches and as a practical tool to shape the attention and guide the actions of risk managers.

EMPIRICAL EVALUATION OF CLASSIFIERS FOR SOFTWARE RISK MANAGEMENT

2009 ◽  
Vol 08 (04) ◽  
pp. 749-767 ◽  
Author(s):  
YI PENG ◽  
GANG KOU ◽  
GUOXUN WANG ◽  
HONGGANG WANG ◽  
FRANZ I. S. KO
Keyword(s):  
Risk Management ◽  
Software Development ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Performance Metric ◽  
Software Modules ◽  
Software Risk Management ◽  
Software Risk ◽  
Prediction Techniques

Software development involves plenty of risks, and errors exist in software modules represent a major kind of risk. Software defect prediction techniques and tools that identify software errors play a crucial role in software risk management. Among software defect prediction techniques, classification is a commonly used approach. Various types of classifiers have been applied to software defect prediction in recent years. How to select an adequate classifier (or set of classifiers) to identify error prone software modules is an important task for software development organizations. There are many different measures for classifiers and each measure is intended for assessing different aspect of a classifier. This paper developed a performance metric that combines various measures to evaluate the quality of classifiers for software defect prediction. The performance metric is analyzed experimentally using 13 classifiers on 11 public domain software defect datasets. The results of the experiment indicate that support vector machines (SVM), C4.5 algorithm, and K-nearest-neighbor algorithm ranked the top three classifiers.

Project-Based Software Risk Management Approaches

2011 ◽  
pp. 3142-3146 ◽  
Author(s):  
Subhas C. Misra ◽  
Vinod Kumar ◽  
Uma Kumar
Keyword(s):  
Risk Management ◽  
Software Engineering ◽  
Software Development ◽  
20Th Century ◽  
21St Century ◽  
Software Industry ◽  
Software Risk Management ◽  
Functional Software ◽  
Management Approaches ◽  
The Cost

The last few decades—especially the end of 20th century and the beginning of 21st century—have shown an increase in the interest in automation of different activities. Automation is dependent in its core on sound functional software. The complexity of software development has increased significantly over the years. Articles showing the failure of projects in the software industry are not surprising. Standish Group (1994) reports show that about 53% of projects get completed, but they do not meet the cost and schedule requirements, and about 31% are canceled before the completion of the projects. These failure reports are significantly alarming. With the tremendous growth in the complexity of software development in the last 10 to 15 years, the management of risks in software engineering activities is becoming an important and nontrivial issue from three perspectives: project, process, and product. Therefore, researchers and practitioners are continually trying to find effective risk management approaches. This article should help the academicians, researchers, and practitioners interested in the area of risk management in software engineering to gain an overall understanding of the area.

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

2020 ◽  
Vol 11 (1) ◽  
pp. 52-62
Author(s):  
Alankrita Aggarwal ◽  
Kanwalvir Singh Dhindsa ◽  
P. K. Suri
Keyword(s):  
Risk Management ◽  
Management Strategies ◽  
Empirical Evaluation ◽  
Software Project ◽  
Key Factors ◽  
Testing Strategies ◽  
Risk Management Strategies ◽  
Benchmark Datasets ◽  
Software Risk Management ◽  
Software Risk

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.

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

2020 ◽  
Vol 12 (2) ◽  
pp. 44-54
Author(s):  
Alankrita Aggarwal ◽  
Kanwalvir S. Dhindsa ◽  
P. K. Suri
Keyword(s):  
Risk Management ◽  
Soft Computing ◽  
Prediction Models ◽  
Multiple Parameters ◽  
Simulated Environment ◽  
Microbiological Process ◽  
Software Risk Management ◽  
Software Risk ◽  
Defect Prediction Models ◽  
Architecture And Design

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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