Mining domain knowledge from app descriptions

Domain analysis aims at obtaining knowledge to a particular domain in the early stage of software development. A key challenge in domain analysis is to extract features automatically from related product artifacts. Compared with other kinds of artifacts, high volume of descriptions can be collected from app marketplaces (such as Google Play and Apple Store) easily when developing a new mobile application (App), so it is essential for the success of domain analysis to obtain features and relationship from them using data technologies. In this paper, we propose an approach to mine domain knowledge from App descriptions automatically. In our approach, the information of features in a single app description is firstly extracted and formally described by a Concern-based Description Model (CDM), this process is based on predefined rules of feature extraction and a modified topic modeling method; then the overall knowledge in the domain is identified by classifying, clustering and merging the knowledge in the set of CDMs and topics, and the results are formalized by a Data-based Raw Domain Model (DRDM). Furthermore, we propose a quantified evaluation method for prioritizing the knowledge in DRDM. The proposed approach is validated by a series of experiments

Recommending software features to designers: From the perspective of users

With lots of public software descriptions emerging in the application market, it is significant to extract common software features from these descriptions and recommend them to new designers. However, existing approaches often recommend features according to their frequencies which reflect designers’ preferences. In order to identify those users’ favorite features and help design more popular software, this paper proposes to make use of the public data of users’ ratings and products’ downloads which reflect users’ preferences to recommend extracted features. The proposed approach distinguishes users’ perspective from designers’ perspective and argues that users’ perspective is better for recommending features because most products are designed for users and expect to be popular among users. Based on the lasso regression to estimate the relationship between the extracted features and the users’ ratings, it proposes to first distinguish the extracted features to identify those rec- ommendable and undesirable features. By treating each download as a support from users to the product features, it further mines the feature association rules from users’ perspective for recommending features. By taking the public data on the market of SoftPedia.com for evaluation, our empirical studies indicate that: (1) selecting recommendable features by lasso regression is better than that by feature frequencies in terms of F1 measure; and (2) recommending features based on the feature association rules mined from users’ perspective is not only feasible but also has competitive performance compared with that based on the rules mined from designs’ perspective in terms of F1 measure

Software Engineering in the Age of App Stores: Feature-Based Analyses to Guide Mobile Software Engineers

Mobile app stores are becoming the dominating distribution platform of mobile applications. Due to their rapid growth, their impact on software engineering practices is not yet well understood. There has been no comprehensive study that explores the mobile app store ecosystem's effect on software engineering practices. Therefore, this thesis, as its first contribution, empirically studies the app store as a phenomenon from the developers' perspective to investigate the extent to which app stores affect software engineering tasks. The study highlights the importance of a mobile application's features as a deliverable unit from developers to users. The study uncovers the involvement of app stores in eliciting requirements, perfective maintenance and domain analysis in the form of discoverable features written in text form in descriptions and user reviews. Developers discover possible features to include by searching the app store. Developers, through interviews, revealed the cost of such tasks given a highly prolific user base, which major app stores exhibit. Therefore, the thesis, in its second contribution, uses techniques to extract features from unstructured natural language artefacts. This is motivated by the indication that developers monitor similar applications, in terms of provided features, to understand user expectations in a certain application domain. This thesis then devises a semantic-aware technique of mobile application representation using textual functionality descriptions. This representation is then shown to successfully cluster mobile applications to uncover a finer-grained and functionality-based grouping of mobile apps. The thesis, furthermore, provides a comparison of baseline techniques of feature extraction from textual artefacts based on three main criteria: silhouette width measure, human judgement and execution time. Finally, this thesis, in its final contribution shows that features do indeed migrate in the app store beyond category boundaries and discovers a set of migratory characteristics and their relationship to price, rating and popularity in the app stores studied

Identifying reusable knowledge in developer instant messaging communication.

Context and background: Software engineering is a complex and knowledge-intensive activity. Required knowledge (e.g., about technologies, frameworks, and design decisions) changes fast and the knowledge needs of those who design, code, test and maintain software constantly evolve. On the other hand, software developers use a wide range of processes, practices and tools where developers explicitly and implicitly “produce” and capture different types of knowledge. Problem: Software developers use instant messaging tools (e.g., Slack, Microsoft Teams and Gitter) to discuss development-related problems, share experiences and to collaborate in projects. This communication takes place in chat rooms that accumulate potentially relevant knowledge to be reused by other developers. Therefore, in this research we analyze whether there is reusable knowledge in developer instant messaging communication by exploring (a) which instant messaging platforms can be a source of reusable knowledge, and (b) software engineering themes that represent the main discussions of developers in instant messaging communication. We also analyze how this reusable knowledge can be identified with the use of topic modeling (a natural language processing technique to discover abstract topics in text) by (c) surveying the literature on how topic modeling has been applied in software engineering research, and (d) evaluating how topic models perform with developer instant messages. Method: First, we conducted a Field Study through an exploratory case study and a reflexive thematic analysis to check whether there is reusable knowledge in developer instant messaging communication, and if so, what this knowledge (main themes discussed) is. Then, we conducted a Sample Study to explore how reusable knowledge in developer instant messaging communication can we identified. In this study, we applied a literature survey and software repository mining (i.e. short text topic modeling). Findings and contributions: We (a) developed a comparison framework for instant messaging tools, (b) identified a map of the main themes discussed in chat rooms of an instant messaging tool (Gitter, a platform used by software developers), (c) provided a comprehensive literature review that offers insights and references on the use of topic modeling in software engineering, and (d) provided an evaluation of the performance of topic models applied to developer instant messages based on topic coherence metrics and human judgment for topic quality