3 research outputs found
User Review-Based Change File Localization for Mobile Applications
In the current mobile app development, novel and emerging DevOps practices
(e.g., Continuous Delivery, Integration, and user feedback analysis) and tools
are becoming more widespread. For instance, the integration of user feedback
(provided in the form of user reviews) in the software release cycle represents
a valuable asset for the maintenance and evolution of mobile apps. To fully
make use of these assets, it is highly desirable for developers to establish
semantic links between the user reviews and the software artefacts to be
changed (e.g., source code and documentation), and thus to localize the
potential files to change for addressing the user feedback. In this paper, we
propose RISING (Review Integration via claSsification, clusterIng, and
linkiNG), an automated approach to support the continuous integration of user
feedback via classification, clustering, and linking of user reviews. RISING
leverages domain-specific constraint information and semi-supervised learning
to group user reviews into multiple fine-grained clusters concerning similar
users' requests. Then, by combining the textual information from both commit
messages and source code, it automatically localizes potential change files to
accommodate the users' requests. Our empirical studies demonstrate that the
proposed approach outperforms the state-of-the-art baseline work in terms of
clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table
Boosting API Recommendation with Implicit Feedback
Developers often need to use appropriate APIs to program efficiently, but it
is usually a difficult task to identify the exact one they need from a vast of
candidates. To ease the burden, a multitude of API recommendation approaches
have been proposed. However, most of the currently available API recommenders
do not support the effective integration of users' feedback into the
recommendation loop. In this paper, we propose a framework, BRAID (Boosting
RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and
active learning techniques to boost recommendation performance. By exploiting
users' feedback information, we train a learning-to-rank model to re-rank the
recommendation results. In addition, we speed up the feedback learning process
with active learning. Existing query-based API recommendation approaches can be
plugged into BRAID. We select three state-of-the-art API recommendation
approaches as baselines to demonstrate the performance enhancement of BRAID
measured by Hit@k (Top-k), MAP, and MRR. Empirical experiments show that, with
acceptable overheads, the recommendation performance improves steadily and
substantially with the increasing percentage of feedback data, comparing with
the baselines.Comment: 15 pages, 4 figure