Fitzgerald, CEB; (2012) Structured Discussion and Early Failure Prediction in Feature Requests. Doctoral thesis , UCL (University College London).

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Abstract

Feature request management systems are popular tools for gathering and negotiating stakeholders' change requests during system evolution. While these frameworks encourage stakeholder participation in distributed software development, their lack of structure also raises challenges. We present a study of requirements defects and failures in large scale feature request management systems, which we build upon to propose and evaluate two distinct solutions for key challenges in feature requests. The discussion forums on which feature request management systems are based make it difficult for developers to understand stakeholders' real needs. We propose a tool-supported argumentation framework, DoArgue, that integrates into feature request management systems allowing stakeholders to annotate comments on whether a suggested feature should be implemented. DoArgue aims to help stakeholders provide input into requirements activity that is more effective and understandable to developers. A case study evaluation suggests that DoArgue encapsulates the key discussion concepts on implementing a feature, and requires little additional effort to use. Therefore it could be adopted to clarify the complexities of requirements discussions in distributed settings. Deciding how much upfront requirements analysis to perform on feature requests is another important challenge: too little may result in inadequate functionalities being developed, costly changes, and wasted development effort; too much is a waste of time and resources. We propose an automated tool-supported framework for predicting failures early in a feature request's life-cycle when a decision is made on whether to implement it. A cost-benefit model assesses the value of conducting additional requirements analysis on a body of feature requests predicted to fail. An evaluation on six large-scale projects shows that prediction models provide more value than the best baseline predictors for many failure types. This suggests that failure prediction during requirements elicitation is a promising approach for localising, guiding, and deciding how much requirements analysis to conduct.

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