Robust Design for Feature Usage-Driven Product Improvement
Date
2018-10-17
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Abstract
Software product improvement is a multi-layered area of study in software engineering, consisting of various processes and attributes. Innovations in technology, changes in customer and market requirements, as well as variations in user requirements introduce challenges, making it difficult to improve a product and satisfy all stakeholders. These concerns become crucial as more features are offered and resources become scarce. To survive in today’s competitive market, product creators need to do more than just keep their customers satisfied. They must also keep them excited for additions and improvements in future releases. The goal is to increase the overall product value; improve product quality and deliver new features to customers. To find an equilibrium between cost and value, and efficiently deploy scarce resources, we can analyze product improvement with respect to system utilization. In this thesis, an exploratory analytical approach is proposed which incorporates robust experimental designs to build the Robust Predictive Performance Model (RPPM) for feature improvement. Robust experimental designs were first introduced by Dr. Genichi Taguchi in the field of manufacturing to increase the robustness of products or processes. Robustness reduces the impact of uncontrollable factors or noises acting on the product and brings the product or process under statistical control. RPPM applies Taguchi designs in software engineering and extends it to generate a reliable predictive linear regression model, using feature usage as an attribute for quality success, customer satisfaction, and overall product value. These findings can then be analyzed to have an unbiased and reliable evaluation of the product value. As part of this thesis, a case study was performed, applying RPPM on a product created by Calgary-based start-up Brightsquid Inc, a global provider of health communication services to medical and dental professionals. Results for applying RPPM show that this approach can lead to reliable predictions about product value, creating a framework for enhancing decision-making, resource allocations, and the ability to accurately forecast future returns in a frequently changing market.
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Keywords
Software Product Improvement, Robust Experimental Designs, Exploratory Research in Software Engineering, Feature-Usage Analysis, Product Performance Predictive Model, Software Engineering
Citation
Hemmati, A. (2018). Robust Design for Feature Usage-Driven Product Improvement (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/33214