Real production code contains lots of knowledge - on the domain, on the architecture, and on the environment. How can we leverage this knowledge in new projects? Using a novel lightweight source code parser, we have mined more than 6,000 open source Linux projects (totaling 200,000,000 lines of code) to obtain 16,000,000 temporal properties reflecting normal interface usage. New projects can be checked against these rules to detect anomalies - that is, code that deviates from the wisdom of the crowds. In a sample of 20 projects, ~25% of the top-ranked anomalies uncovered actual code smells or defects.

A caller must satisfy the callee's precondition--that is, reach a state in which the callee may be called. Preconditions describe the state that needs to be reached, but not how to reach it. We combine static analysis with model checking to mine Computation Tree Logic (CTL) formulas that describe the operations a parameter goes through: "In parseProperties (String xml), the parameter xml normally stems from getProperties()." Such operational preconditions can be learned from program code, and the code can be checked for their violations. Applied to AspectJ, our Tikanga prototype found 189 violations of operational preconditions, uncovering 9 unique defects and 36 unique code smells-with 44% true positives in the 50 top-ranked violations.

Interacting with objects often requires following a protocol—for instance, a specific sequence of method calls. These protocols are not always documented, and violations can lead to subtle problems. Our approach takes code examples to automatically infer legal sequences of method calls. The resulting patterns can then be used to detect anomalies such as “Before calling next(), one normally calls hasNext()”. To our knowledge, this is the first fully automatic defect detection approach that learns and checks method call sequences. Our JADET prototype has detected yet undiscovered defects and code smells in five popular open-source programs, including two new defects in ASPECTJ.