Interpretive structural modelling for attributes of software quality

Abstract

Purpose The “quality” of any product or service defines the agility of the product and its life cycle in dynamic environment. The demand of high “quality” becomes an imperative concern, when “software” is acting as a product or a service. Since the nature of the software is intangible and more complex, therefore the assurance of providing accurate results is anxiety for companies. The overall quality of the software is based upon many individual factors (or attributes) that makes software reliable, inclined and a long-lasting product in the marketplace. But how these factors can influence each other is significant to identify. Therefore, the purpose of this paper is to study the quality aspect of the software and analyse the interrelationship of impactful attributes. Design/methodology/approach The analysis has been done through responses sought from software development teams/clients in India. The questionnaire related to the software quality was administered to the sample population. Interconnection among impactful characteristics has been analysed by using a qualitative technique called interpretive structural modelling (ISM). The driving and dependency of the attributes under consideration has been classified using cross-impact matrix multiplication applied to classification (MICMAC) analysis. The procedure of applying ISM method has been automated and provided it as package “ISM” using R software. Findings In general, it is very complex job to determine the most impactful attribute of software quality. By applying ISM and MICMAC analysis on the set of attributes under consideration, it has been found that “reliability” along with “usability” and “performance” is the most influential attribute of software quality and preferred most. Research limitations/implications Though ISM provides an organized modelling framework yet its results are considered less statistically significant. Therefore, it would be interesting to concatenate the present findings with the findings of any analytical methodology; which gives statistically significant results. Practical implications The present proposal deals with the interpretation of the software quality attributes and their contextual relationship but with more effective and efficient manner. It can help management to understand the complexity of relationship amongst attributes (which are quality attributes here) more accurately and precisely. Since today is an era of automation, the manual part is being substituted so as to reduce the labour cost, improve safety, security and product quality to increase production. This study is, therefore, an effort and a helping hand in making the hassle free calculations for obtaining intermediate matrices and doing eventual calculations. Social implications n numbers of parameters can be selected to analyse the interrelationship of any project/study. Eradication human errors in applying transitivity law or applying any other operation in solving problem. The package created here can save precious time of users. Provides well-formatted and readable excel output files that make interpretation easier. Originality/value Software is one such product/service which plays a significant role in this high-technological world, where each and every firm try their best to be on the top of the list of consumers’ preference. For this purpose, companies reduce manual efforts by converting it into qualitative software that provides deliverables in a systematic manner. Therefore, it becomes imperative to study various interrelated quality attributes of the software. On the similar platform, ISM is a widely used technique and just to provide a helping hand in quantification of the qualitative attributes this paper facilitates the readers with algorithm developed using R software.

SmartISM: Implementation and Assessment of Interpretive Structural Modeling

“…FRM also consists of driving and dependence powers of each variable by counting 1s and 1*s in rows and columns respectively. Very few studies mention usage of some software for transitivity calculations such as [56,57]. However, one of the most frequent reasons for incorrect ISM calculations have been wrong transitivity calculations, such as in studies [9,13,16,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37].…”

“…Previous ISM reviews [53][54][55] don't critically analyze the steps of ISM applications in the articles. Similarly, although some automation of the ISM technique has been provided earlier [56,57], there does not exist any end-to-end graphical software that may help in applying this technique and allow the decision makers to focus on sharing knowledge and iterate the ISM technique until a high-confidence consensus model is arrived at. These challenges set the objectives of this research as follows:…”

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“…The algorithm used and the profound mathematical foundations of ISM were described in detail in [60]. For the purpose of this study, the binary ISM input matrices were processed by means of the ISM package of the statistic software R [61].…”

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“…The dependence power is the sum of factors which possibly influence it plus itself [9]. Singh and Kant [8] explained this as follows by substituting V, A, X, and O with 1 or 0 used in SSIM [10].…”