Parallel methods for evidence and trust based selection and recommendation of software apps from online marketplaces

Abstract

With the popularity of various online software marketplaces, thirdparty vendors are creating many instances of software applications ('apps') for mobile and desktop devices targeting the same set of requirements. This abundance makes the task of selecting and recommending (S&R) apps, with a high degree of assurance, for a specific scenario a significant challenge. The S&R process is a precursor for composing any trusted system made out of such individually selected apps. In addition to feature-based information, about these apps, these marketplaces contain large volumes of user reviews. These reviews contain unstructured user sentiments about app features and the onus of using these reviews in the S&R process is put on the user. This approach is ad-hoc, laborious and typically leads to a superficial incorporation of the reviews in the S&R process by the users. However, due to the large volumes of such reviews and associated computing, these two techniques are not able to provide expected results in real-time or near real-time. Therefore, in this paper, we present two parallel versions (i.e., batch processing and stream processing) of these algorithms and empirically validate their performance using publically available datasets from the Amazon and Android marketplaces. The results of our study show that these parallel versions achieve near real-time performance, when measured as the end-to-end response time, while selecting and recommending apps for specific queries. CCS CONCEPTS• Information systems ~ Trust • Information systems R ecommender systems • Social and professional topics ~ Software selection and adaptation

A Holistic Ranking Scheme for Apps

“…Only a few efforts have quantified trust tuples based on the reviews of apps [32,33]. As indicated earlier, these efforts have either not considered large datasets or not created a combined view of the apps.…”

Disparity between the programmatic views and the user perceptions of mobile apps

“…Hammad et al [16] used FindBugs to determine which categories of bugs occurred more frequently in low rated apps rather than in high rated apps by examining the relationships between each category of bugs in an app and the corresponding app rating. In our work, we have used FindBugs to identify different categories of bugs in terms of bug ranks (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and bug confidence levels (such as high, medium and low). The bug rank represents the severity of the bug and the confidence level indicates confidence of the tool regarding the bug existences.…”

“…), and an external view that uses the nonprogrammatic artifacts (e.g., user ratings and reviews in public marketplaces). In our past work ( [4,5]), we have focused on the external view to quantify the trust of an app using evidence-based techniques (such as theory of belief [6], and associated NLP schemes [7]). The trust of an app is represented as a tuple of belief, disbelief and uncertainty (B, D, U).…”