Integrated Software Quality Evaluation: A Fuzzy Multi-Criteria Approach

Challa, Jagat Sesh; Paul, Arindam; Dada, Yogesh; Nerella, Venkatesh; Srivastava, Praveen Ranjan; Singh, Ajit Pratap

doi:10.3745/jips.2011.7.3.473

Cited by 45 publications

(57 citation statements)

References 32 publications

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“…vendor in the case. Challa et al [23] have developed a tool based upon the algorithm using Fuzzy AHP as the base for selecting the quality parameters. They considered the developer's perspective, the user's perspective, and the project manager's perspective.…”

Section: Quality Evaluationmentioning

confidence: 99%

Applications of Multi-criteria Decision Making in Software Engineering

Sehra¹,

Brar²,

Kaur³

2016

ijacsa

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Abstract-Every complex problem now days require multicriteria decision making to get to the desired solution. Numerous Multi-criteria decision making (MCDM) approaches have evolved over recent time to accommodate various application areas and have been recently explored as alternative to solve complex software engineering problems. Most widely used approach is Analytic Hierarchy Process that combines mathematics and expert judgment. Analytic Hierarchy Process suffers from the problem of imprecision and subjectivity. This paper proposes to use Fuzzy AHP (FAHP) instead of traditional AHP method. The usage of FAHP helps decision makers to make better choices both in relation to tangible criteria and intangible criteria. The paper provides a clear guide on how FAHP can be applied, particularly in the software engineering area in specific situations. The conclusion of this study would help and motivate practitioners and researchers to use multi-criteria decision making approaches in the area of software engineering.

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Section: Quality Evaluationmentioning

confidence: 99%

Applications of Multi-criteria Decision Making in Software Engineering

Sehra¹,

Brar²,

Kaur³

2016

ijacsa

View full text Add to dashboard Cite

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“…A linguistic term can be defined as a variable and the importance weight can be evaluated by linguistic terms such as very low, low, average, high and very high. For every complexity attribute, there is a corresponding importance weight and rating [5] [8]. The linguistic terms can be expressed as a triangular fuzzy numbers for ratings and weight as in Table1 and Table2.…”

Section: Fuzzification Processmentioning

confidence: 99%

“…Defuzzification process converts fuzzy output into crisp output using centroid method [8]. The centroid formula is as follows:…”

Section: Defuzzification Processmentioning

confidence: 99%

“…Non-Functional Requirement Complexity weight is based on the importance of quality characteristics [8]. …”

Section: Non-functional Requirement Complexitymentioning

confidence: 99%

“…Multi-criteria Decision Method deals with the process of making decisions in the presence of multiple criteria or objectives [8] [14] [19]. A decision maker (DM) is required to make decisions among multiple criteria which can be qualitative or quantitative.…”

Section: Background On Multi-criteria Fuzzy Based Approachmentioning

confidence: 99%

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Prediction of Software Requirements Stability Based on Complexity Point Measurement Using Multi-Criteria Fuzzy Approach

Francis¹,

Christopher²

2012

IJSEA

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Many software projects fail due to instable requirements and lack of managing the requirements changes efficiently. Software Requirements Stability Index Metric (RSI) helps to evaluate the overall stability of requirements and also keep track of the project status. Higher the stability, less changes tends to propagate. The existing system use Function Point modeling for measuring the Requirements Stability. However, the main drawback of the existing modeling is that the complexity of non-functional requirements has not been measured for Requirements KEYWORDS: Multi-Criteria Fuzzy Based Approach, Functional Requirement Complexity, Non-Functional Requirement Complexity, Input Output Complexity, Interface Complexity, File Complexity, Software Complexity Point Measurement, Requirements Stability Index. INTRODUCTIONRequirements Elicitation is the most important stage in the Software Development Life Cycle Process. If the requirements have not been captured correctly during the Elicitation process, then the whole development process will fail which results in time and monetary costs [18]. Software developers often start with unclear, ambiguous, and incomplete requirements with inaccurate understanding of the user needs or insufficient requirements. Therefore, requirements development and management are the start point of the software development process. Software system needs to evolve. In particular, changes in the requirements may be related to the addition of new functionalities, modification to the existing ones, deleting the functionalities which are International Journal of Software Engineering & Applications (IJSEA), Vol.3, No.6, November 2012 102 obsolete or to the improvement in the quality of service offered [10]. Requirements changes not only cause software defects but also cause in delay of delivery of the software project. Requirements changes at the later stage can cause uncertainty in the software development. Sometimes, these requirement changes will affect the quality of the software. For requirements engineering, the challenging issue is not the requirements change. It is how to deal with the change and how to measure them. For measuring the change, measure the software complexity. IEEE defines software complexity as "the degree to which a system or component has a design or implementation that is difficult to understand or verify" [1].Software Complexity Measurement can be classified into three categories: Size, Structure and Quality Measurement [17]. In present, there are many algorithmic models and non-algorithmic models have been developed to measure the complexity of the software [20]. Some of the famous algorithmic models are Function Point Modeling, Constructive Cost Model (COCOMO), Software Life Cycle Management model (SLIM). Non-algorithmic techniques include Price-toWin, Expert Judgment and Machine learning approaches. Machine Learning is used to group together a set of techniques that embody some of the facets of human mind. For example, fuzzy systems, analogy, regressi...

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