Data-Driven Analysis towards Monitoring Software Evolution by Continuously Understanding Changes in Users’ Needs

Software products, though always being expected to provide satisfactory functionalities and be bug-free, somehow fail to meet the expectations of their users. Thus, software maintenance is inevitable and critical for any software companies who want their products or services to continue profiting. On the other hand, due to the fierce competitiveness in the contemporary software market, as well as the ease of user churns, monitoring and sustaining the satisfaction of the users is a critical criterion for the long-term success of any software products within their evolution stage. To such an end, continuously understanding and meeting the users’ needs and expectations is the key, as it is more efficient and effective to allocate maintenance effort accordingly to address the issues raised by users.

On the other hand, accompanied by the rapid development of internet technologies, the volume of user-generated content has been increasing exponentially. Among such user-generated content, feedback from the customers, either numeric rating, recommendation, or textual reviews, have been playing an increasingly critical role in product designs in terms of understanding customers’ needs. Especially for software products that require constant maintenance and are continuously evolving, understanding of users’ needs and complaints, as well as the changes in their opinions through time, is of great importance. Additionally, supported by the advance of data mining and machine learning techniques, the effort of knowledge discovery from analyzing such data and specially understanding the behavior of the users shall be largely reduced.

However, though many studies propose data-driven approaches for feedback analysis, the ones specifically on applying such methods supporting software maintenance and evolution are limited. Many studies focus on the text mining perspective of review analysis towards eliciting users’ opinions. Many others focus on the detection and classification of feedback types, e.g., feature requests, bug reports, and emotion expression, etc. For the purpose of enhancing the effectiveness in soft ware maintenance and evolution practice, an effective approach on the software’s perceived user experience and the monitoring of its changes during evolution is re- quired.

To support the practice of software maintenance and evolution targeting enhancing user satisfaction, we propose a data-driven user review analysis approach. The contribution of this research aims to answer the following research questions: RQ1. How to analyze users’ collective expectation and perceived quality in use with data- driven approaches by exploiting sentiment and topics? RQ2. How to monitor user satisfaction over software updates during software evolution using reviews’ topics and sentiments? RQ3. How to analyze users’ profiles, software types and situational contexts as contexts of use that supports the analysis of user satisfaction? Towards answering RQ1, the thesis proposes a data-driven approach of user perceived quality evaluation and users’ needs extraction via sentiment analysis and topic modeling on large volume of user review data. Based on such outcome, the answer to RQ2 encompasses of 1) the approach to monitor user opinion changes through software evolution by detecting similar topic pairs and 2) the approach to identify the problematic updates based on anomalies in review sentiment distribution. Towards the answer to RQ3, a three-fold analysis is proposed: 1) situational contexts and ways of interaction analysis, 2) user profile and preference analysis and 3) software type and related features analysis. All the above approaches are validated by case studies.

This thesis contributes to the examination of applying data-driven end user re- view analysis methods supporting software maintenance and evolution. The main implication is to enrich the existing domain knowledge of software maintenance and evolution in terms of taking advantage of the collective intelligence of end users. In addition, it conveys unique contribution to the research on software evolution con- texts in terms of various meaningful aspects and leads to a potential interdisciplinary contribution as well. On the other hand, this thesis also contributes to software maintenance and evolution practice even in the larger scope of the software industry by proposing an effective series of approaches that address critical issues within. It helps the developers ease their effort in release planning and other decision-making activities.