NotOnlyLog: Mining Patch-Log Associations from Software Evolution History to Enhance Failure Diagnosis Capability

Log messages are widely used in the diagnosis of software failures. Existing studies of failure diagnosis based on log messages tend to use rule-based methods or execution-path-based methods. Rule-based methods generate bug-fixing rules using either human expertise, which is time consuming, or machine learning methods, which may lack the precision of failure diagnosis. To remedy these problems, researchers propose execution-path-based methods that reconstruct execution paths by analyzing source code and run-time logs. These methods, however, may lead to path explosion. To fill this gap, our work focuses on solving the path explosion problem in execution-path-based methods. We assume that run-time logs may have a relationship with their corresponding patches in real-world bug reports. We conduct empirical studies on seven open-source software packages and obtain two findings: (1) 80% of similar bugs have similar patches, and (2) 70% of faulty code is found to lie near the code where the first failure message is printed. Based on these two observations, we design and implement a practical tool NotOnlyLog for bug diagnosis. NotOnlyLog is able to mine the relationships between failure logs and their corresponding patches, in order to reduce both the number and length of uncertain execution paths in bug diagnosis. We evaluate the performance of NotOnlyLog on nine real-world bugs from three large open-source projects. Our experimental results show that, compared with SherLog, NotOnlyLog can achieve a reduction of 86.9% in the number of execution paths.