Fault based testing is a technique in which test cases are chosen to reveal certain classes of faults. At present, testing professionals use their personal experience to select testing methods for fault classes considered the most likely to be present. However, there is little empirical evidence available in the open literature to support these intuitions. By examining the source code changes when faults were fixed in seven open source software artifacts, we have classified bug fix patterns into fault classes, and recorded the relative frequencies of the identified fault classes. This paper reports our findings related to "if-conditional" fixes. We have classified the "if-conditional" fixes into fourteen fault classes and calculated their frequencies. We found the most common fault class related to changes within a single "atom". The next most common fault was the omission of an "atom". We analysed these results in the context of Boolean specification testing.
Many techniques have been developed over the years to automatically find bugs in software. Often, these techniques rely on formal methods and sophisticated program analysis. While these techniques are valuable, they can be difficult to apply, and they aren't always effective in finding real bugs. Bug patterns are code idioms that are often errors. We have implemented automatic detectors for a variety of bug patterns found in Java programs. In this extended abstract1, we describe how we have used bug pattern detectors to find serious bugs in several widely used Java applications and libraries. We have found that the effort required to implement a bug pattern detector tends to be low, and that even extremely simple detectors find bugs in real applications. From our experience applying bug pattern detectors to real programs, we have drawn several interesting conclusions. First, we have found that even well tested code written by experts contains a surprising number of obvious bugs. Second, Java (and similar languages) have many language features and APIs which are prone to misuse. Finally, that simple automatic techniques can be effective at countering the impact of both ordinary mistakes and misunderstood language features.