Towards just-in-time suggestions for log changes

Software developers typically insert logging statements in their source code to record runtime information. However, providing proper logging statements remains a challenging task. Prior approaches automatically enhance logging statements, as a post-implementation process. Such automatic approaches do not take into account developers’ domain knowledge; nevertheless, developers usually need to carefully design the logging statements since logs are a rich source about the field operation of a software system. The goals of this paper include: i) understanding the reasons for log changes; and ii) proposing an approach that can provide developers with log change suggestions as soon as they commit a code change, which we refer to as “just-in-time” suggestions for log changes. In particular, we derive a set of measures based on manually examining the reasons for log changes and our experiences. We use these measures as explanatory variables in random forest classifiers to model whether a code commit requires log changes. These classifiers can provide just-in-time suggestions for log changes. We perform a case study on four open source projects: Hadoop, Directory Server, Commons HttpClient, and Qpid. We find that: (i) The reasons for log changes can be grouped along four categories: block change, log improvement, dependence-driven change, and logging issue; (ii) our random forest classifiers can effectively suggest whether a log change is needed: the classifiers that are trained from within-project data achieve a balanced accuracy of 0.76 to 0.82, and the classifiers that are trained from cross-project data achieve a balanced accuracy of 0.76 to 0.80; (iii) the characteristics of code changes in a particular commit and the current snapshot of the source code are the most influential factors for determining the likelihood of a log change in a commit.