Robust Design for Feature Usage-Driven Product Improvement

Hemmati, Ashkan

Robust Design for Feature Usage-Driven Product Improvement

dc.contributor.advisor	Ruhe, Guenther
dc.contributor.author	Hemmati, Ashkan
dc.contributor.committeemember	Saunders, Chad W.
dc.contributor.committeemember	Rokne, Jon G.
dc.date	2019-06
dc.date.accessioned	2018-10-18T15:21:09Z
dc.date.available	2018-10-18T15:21:09Z
dc.date.issued	2018-10-17
dc.description.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.	en_US
dc.identifier.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	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/33214
dc.identifier.uri	http://hdl.handle.net/1880/108893
dc.language.iso	eng
dc.publisher.faculty	Graduate Studies
dc.publisher.faculty	Science
dc.publisher.institution	University of Calgary	en
dc.publisher.place	Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Software Product Improvement
dc.subject	Robust Experimental Designs
dc.subject	Exploratory Research in Software Engineering
dc.subject	Feature-Usage Analysis
dc.subject	Product Performance Predictive Model
dc.subject	Software Engineering
dc.subject.classification	Computer Science	en_US
dc.title	Robust Design for Feature Usage-Driven Product Improvement
dc.type	master thesis
thesis.degree.discipline	Computer Science
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.item.requestcopy	true