Topic Modeling Based Warning Prioritization from Change Sets of Software Repository  

作　　者：Jung-Been Lee Taek Lee Hoh Peter In 

机构地区：[1]College of Informatics,Korea University,Seoul 02841,Korea [2]College of Knowledge-Based Services Engineering,Sungshin University,Seoul 02844,Korea

出　　处：《Journal of Computer Science & Technology》2020年第6期1461-1479,共19页计算机科学技术学报（英文版）

基　　金：The research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT&Future Planning under Grant No.NRF-2019R1A2C2084158;Samsung Electronics Co.Ltd.

摘　　要：Many existing warning prioritization techniques seek to reorder the static analysis warnings such that true positives are provided first. However, excessive amount of time is required therein to investigate and fix prioritized warnings because some are not actually true positives or are irrelevant to the code context and topic. In this paper, we propose a warning prioritization technique that reflects various latent topics from bug-related code blocks. Our main aim is to build a prioritization model that comprises separate warning priorities depending on the topic of the change sets to identify the number of true positive warnings. For the performance evaluation of the proposed model, we employ a performance metric called warning detection rate, widely used in many warning prioritization studies, and compare the proposed model with other competitive techniques. Additionally, the effectiveness of our model is verified via the application of our technique to eight industrial projects of a real global company.

关 键 词：automated static analysis topic modeling warning prioritization 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]