METHODOLOGICAL EVALUATION ON CLINICAL RESEARCH LITERATURE OF ACUPUNCTURE TREATMENT OF FACIAL PARALYSIS

Objective: To try to give an objective evaluation on the clinical research situation about acupuncture treatment of facial paralysis in the past 50 years and try to provide a possible evidence for clinical practice. Methods: All papers are searched and assessed according to the international standards and clinical epidemiology. Results: There is no systematic review (SR) on acupuncture treatment of facial palsy in a total of 1021 articles enlisted in the present paper. Comparing with the quantity of the descriptive studies and expert opinions (constituting 84.84%), that of the randomized controlled trials (RCTs) and clinical controlled trials (CCTs) is smaller (constituting 15.16%), besides, the quality of RCTs and CCTs is unsatisfactory. Conclusion: At present, the quantity and quality of studies with RCTs about acupuncture treatment of facial paralysis can’t meet the need of clinical practice, and in order to improve the therapeutic effect, a higher quality of RCTs and SR is required.

What Are They Talking About？ Analyzing Code Reviews in Pull-Based Development Model

Code reviews in pull-based model are open to community users on GitHub. Various participants are taking part in the review discussions and the review topics are not only about the improvement of code contributions but also about project evolution and social interaction. A comprehensive understanding of the review topics in pull-based model would be useful to better organize the code review process and optimize review tasks such as reviewer recommendation and pull-request prioritization. In this paper, we first conduct a qualitative study on three popular open-source software projects hosted on GitHub and construct a fine-grained two-level taxonomy covering four level-1 categories （code correctness, pull- request decision-making, project management, and social interaction） and 11 level-2 subcategories （e.g., defect detecting, reviewer assigning, contribution encouraging）. Second, we conduct preliminary quantitative analysis on a large set of review comments that were labeled by TSHC （a two-stage hybrid classification algorithm）, which is able to automatically classify review comments by combining rule-based and machine-learning techniques. Through the quantitative study, we explore the typical review patterns. We find that the three projects present similar comments distribution on each subeategory. Pull-requests submitted by inexperienced contributors tend to contain potential issues even though they have passed the tests. Furthermore, external contributors are more likely to break project conventions in their early contributions.

Preface

As a continuation of the last two years＇ special section on software systems, this special section is another effort to encourage and promote research to address challenges from the perspective of software systems. The goal of this special section is to present state-of-the-art and high-quality original research in the area of software systems. Similar to the last two years＇ special section, this special section includes two major themes： data-driven software engineering and software testing and analysis.