Aspect/Feature-based Evaluation of Competing Apps

Yevhenii Sabanin
Measurement of software quality is a challenge for many companies. It is very complicated to extract and structure experience that users had. The feedback is usually too general, and it is becoming tough to figure out which problems a piece of software has and in which specific features (e.g. security problem, UI). At the same time, customers are struggling with the problem of comparing applications based on their features. There is no way for customers to know exactly which functionality works well, and which does not, in different applications.
Companies are trying to make customers provide "structured" feedback. However, feedback forms are often filled in superficially and partly or completely ignored. Since selling online is nowadays the typical delivery channel of software applications, most of the customer reviews are stored online and thus publicly available on the web (e.g. Google Play, Apple Store – for mobile software). However, automatically extracting valuable information and separating positive from negative opinions, as well as classifying software apps by feature groups is difficult. Comparing competing applications based on features they have is still a hard problem. One of the problems is the large amount of comments, which makes it difficult to keep track of the reviewers’ variety of sentiments. Likewise, it is hard to figure out a summarized opinion about each aspect (also, widely used the word “feature”) of the software. That is why approaches to sentiment analysis are becoming more and more popular. Much research has been done in this field, and various methods and tools have been developed and applied. Based on information extracted from app reviews, in this thesis, I tackle three goals:
1. For a given app, identify features that this software application has.
2. Identify those applications that can be considered competing apps with regards to functionality (i.e., the set of features provided).
3. Compare these applications using sentiment analysis.
Graduation Thesis language
Graduation Thesis type
Master - Software Engineering
Dietmar Pfahl, Faiz Shah
Defence year