Searching for configurations in clone evaluation - A replication study

Clone detection is the process of finding duplicated code within a software code base in an automated manner. It is useful in several areas of software development such as code quality analysis, bug detection, and program understanding.We replicate a study of a geneticalgorithm based framework that optimises parameters for clone agreement (EvaClone). We apply the framework to 14 releases of Mockito, a Java mocking framework. We observe that the optimised parameters outperform the tools’ default parameters in term of clone agreement by 19.91% to 66.43 %. However, the framework gives undesirable results in term of clone quality. EvaClone either maximises or minimises a number of clones in order to achieve the highest agreement resulting in more false positives or false negatives introduced consequently

Raters’ reliability in clone benchmarks construction

International audienceCloned code often complicates code maintenance and evolution and must therefore be effectively detected. One of the biggest challenges for clone detectors is to reduce the amount of irrelevant clones they found, called false positives. Several benchmarks of true and false positive clones have been introduced, enabling tool developers to compare, assess and fine-tune their tools. Manual inspection of clone candidates is performed by raters that do not have expertise on the underlying code. This way of building benchmarks might be unreliable when considering context-dependent clones i.e., clones valid for a specific purpose. Our goal is to investigate the reliability of rater judgments about context-dependent clones. We randomly select about 600 clones from two projects and ask several raters, including experts of the projects, to manually classify these clones. We observe that judgments of non expert raters are not always repeatable. We also observe that they seldomly agree with each others and with the expert. Finally, we find that the project and the fact that a clone is a true or false positive might have an influence on the agreement between the expert and non experts. Therefore, using non experts to produce clone benchmarks could be unreliable