Advanced code coverage analysis using substring holes

Abstract

Code coverage is a common aid in the testing process. It is generally used for marking the source code segments that were executed and, more importantly, those that were not executed. Many code coverage tools exist, supporting a variety of languages and operating systems. Unfortunately, these tools provide little or no assistance when code coverage data is voluminous. Such quantities are typical of system tests and even for earlier testing phases. Drill-down capabilities that look at different granularities of the data, starting with directories and going through files to functions and lines of source code, are insufficient. Such capabilities make the assumption that the coverage issues themselves follow the code hierarchy. We argue that this is not the case for much of the uncovered code. Two notable examples are error handling code and platform-specific constructs. Both tend to be spread throughout the source in many files, even though the related coverage, or lack thereof, is highly dependent. To make the task more manageable, and therefore more likely to be performed by users, we developed a hole analysis algorithm and tool that is based on common substrings in the names of functions. We tested its effectiveness using two large IBM software systems. In both of them, we asked domain experts to judge the results of several hole-ranking heuristics. They found that 57%-87% of the 30 top-ranked holes identified by the effective heuristics are relevant. Moreover, these holes are often unexpected. This is especially impressive because substring hole analysis relies only on the names of functions, whereas domain experts have a broad and deep understanding of the system. We grounded our results in a theoretical framework that states desirable mathematical properties of hole ranking heuristics. The empirical results show that heuristics with these properties tend to perform better, and do so more consistently, than heuristics lacking them.