StoryDroid: Automated Generation of Storyboard for Android Apps

Mobile apps are now ubiquitous. Before developing a new app, the development team usually endeavors painstaking efforts to review many existing apps with similar purposes. The review process is crucial in the sense that it reduces market risks and provides inspiration for app development. However, manual exploration of hundreds of existing apps by different roles (e.g., product manager, UI/UX designer, developer) in a development team can be ineffective. For example, it is difficult to completely explore all the functionalities of the app in a short period of time. Inspired by the conception of storyboard in movie production, we propose a system, StoryDroid, to automatically generate the storyboard for Android apps, and assist different roles to review apps efficiently. Specifically, StoryDroid extracts the activity transition graph and leverages static analysis techniques to render UI pages to visualize the storyboard with the rendered pages. The mapping relations between UI pages and the corresponding implementation code (e.g., layout code, activity code, and method hierarchy) are also provided to users. Our comprehensive experiments unveil that StoryDroid is effective and indeed useful to assist app development. The outputs of StoryDroid enable several potential applications, such as the recommendation of UI design and layout code

Can better identifier splitting techniques help feature location

Abstract — The paper presents an exploratory study of two feature location techniques utilizing three strategies for splitting identifiers: CamelCase, Samurai and manual splitting of identifiers. The main research question that we ask in this study is if we had a perfect technique for splitting identifiers, would it still help improve accuracy of feature location techniques applied in different scenarios and settings? In order to answer this research question we investigate two feature location techniques, one based on Information Retrieval and the other one based on the combination of Information Retrieval and dynamic analysis, for locating bugs and features using various configurations of preprocessing strategies on two open-source systems, Rhino and jEdit. The results of an extensive empirical evaluation reveal that feature location techniques using Information Retrieval can benefit from better preprocessing algorithms in some cases, and that their improvement in effectiveness while using manual splitting over state-of-the-art approaches is statistically significant in those cases. However, the results for feature location technique using the combination of Information Retrieval and dynamic analysis do not show any improvement while using manual splitting, indicating that any preprocessing technique will suffice if execution data is available. Overall, our findings outline potential benefits of putting additional research efforts into defining more sophisticated source code preprocessing techniques as they can still be useful in situations where execution information cannot be easily collected. Keywords-feature location; information retrieval; dynamic analysis; identifier splitting algorithms I

Exploiting Parts-of-Speech for Effective Automated Requirements Traceability

Context: Requirement traceability (RT) is defined as the ability to describe and follow the life of a requirement. RT helps developers ensure that relevant requirements are implemented and that the source code is consistent with its requirement with respect to a set of traceability links called trace links. Previous work leverages Parts Of Speech (POS) tagging of software artifacts to recover trace links among them. These studies work on the premise that discarding one or more POS tags results in an improved accuracy of Information Retrieval (IR) techniques. Objective: First, we show empirically that excluding one or more POS tags could negatively impact the accuracy of existing IR-based traceability approaches, namely the Vector Space Model (VSM) and the Jensen Shannon Model (JSM). Second, we propose a method that improves the accuracy of IR-based traceability approaches. Method: We developed an approach, called ConPOS, to recover trace links using constraint-based pruning. ConPOS uses major POS categories and applies constraints to the recovered trace links for pruning as a filtering process to significantly improve the effectiveness of IR-based techniques. We conducted an experiment to provide evidence that removing POSs does not improve the accuracy of IR techniques. Furthermore, we conducted two empirical studies to evaluate the effectiveness of ConPOS in recovering trace links compared to existing peer RT approaches. Results: The results of the first empirical study show that removing one or more POS negatively impacts the accuracy of VSM and JSM. Furthermore, the results from the other empirical studies show that ConPOS provides 11%-107%, 8%-64%, and 15%-170% higher precision, recall, and mean average precision (MAP) than VSM and JSM. Conclusion: We showed that ConPosout performs existing IR-based RT approaches that discard some POS tags from the input documents