The effects of requirements elicitation issues on software project performance: An empirical analysis

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Abstract

[Context and motivation] Studies have emphasized the need for effective requirements elicitation owing to its significant impacts on software quality and overall project outcomes to meet system objectives. The empirical studies in literature present the relationships between the specific characteristics that affect elicitation and project performance that focus on process control and product flexibility. There is, however, no substantial research on the empirical relationship between the generalized problems in requirements elicitation and project performance. [Question/problem]The issues encountered in requirements elicitation generalized through categories of problems of scope, problems of volatility and problems of understanding. This study aims in establishing an empirical model to study the behavior between the requirements elicitation issues and project performance. This study also validates the model for its consistency with practitioner's views and earlier studies.[Principal ideas/ results] Researchers and practitioners have focused on developing tools and techniques that will enhance the requirements elicitation and analysis phases. However, the effectiveness of the tool usage is dependent on skills and behaviors of people and organization using them. The aspects of behavior are best modeled using techniques adopted in social research, viz. confirmatory factor analysis; the technique is adopted for this study. [Contribution] This study deduced a causal relationship between the requirements elicitation issues and project performance. This study also attempted to establish a priority-setting and decision-making to address elicitation issues that can control and manage residual performance risks. © 2014 Springer International Publishing Switzerland.

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Sethia, N. K., & Pillai, A. S. (2014). The effects of requirements elicitation issues on software project performance: An empirical analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8396 LNCS, pp. 285–300). Springer Verlag. https://doi.org/10.1007/978-3-319-05843-6_21

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