SABER: Identifying SimilAr BEhavioR for Program Comprehension

Modern software engineering practices rely on program comprehension as the most basic underlying component for improving developer productivity and software reliability. Software developers are often tasked to work with unfamiliar code in order to remove security vulnerabilities, port and refactor legacy code, and enhance software with new features desired by users. Automatic identification of behavioral clones, or behaviorally-similar code, is one program comprehension technique that can provide developers with assistance. The idea is to identify other code that “does the same thing” and that may be more intuitive; better documented; or familiar to the developer, to help them understand the code at hand. Unlike the detection of syntactic or structural code clones, behavioral clone detection requires executing workloads or test cases to find code that executes similarly on the same inputs. However, a key problem in behavioral clone detection that has not received adequate attention is the “preponderance of the evidence” problem, which advocates for more convincing evidence from nontrivial test case executions to gain confidence in the behavioral similarities. In other words, similar outputs for some inputs matter more than for others. We present a novel system, SABER, to address the “preponderance of the evidence” problem, for which we adapt the legal metaphor of “more likely to be true than not true” burden of proof. We develop a novel test case generation methodology with three primary dynamic analysis techniques for identifying important behavioral clones. Further, we investigate filtering and weighting schemes to guide developers toward the most convincing behavioral similarities germane to specific software engineering tasks, such as code review, debugging, and introducing new features.