Detection, tracking, and refactoring of code clones (i.e., identical or nearly similar code fragments in the code-base of a software system) have been extensively investigated by a great many studies. Code clones have often been considered bad smells. While clone refactoring is important for removing code clones from the code-base, clone tracking is important for consistently updating code clones that are not suitable for refactoring. In this research we investigate the importance of micro-clones (i.e., code clones of less than five lines of code) in consistent updating of the code-base. While the existing clone detectors and trackers have ignored micro clones, our investigation on thousands of commits from six subject systems imply that around 80% of all consistent updates during system evolution occur in micro clones. The percentage of consistent updates occurring in micro clones is significantly higher than that in regular clones according to our statistical significance tests. Also, the consistent updates occurring in micro-clones can be up to 23% of all updates during the whole period of evolution. According to our manual analysis, around 83% of the consistent updates in micro-clones are non-trivial. As micro-clones also require consistent updates like the regular clones, tracking or refactoring micro-clones can help us considerably minimize effort for consistently updating such clones. Thus, micro-clones should also be taken into proper consideration when making clone management decisions.

The design and maintenance of APIs are complex tasks due to the constantly changing requirements of its users. Despite the efforts of its designers, APIs may suffer from a number of issues (such as incomplete or erroneous documentation, poor performance, and backward incompatibility). To maintain a healthy client base, API designers must learn these issues to fix them. Question answering sites, such as Stack Overflow (SO), has become a popular place for discussing API issues. These posts about API issues are invaluable to API designers, not only because they can help to learn more about the problem but also because they can facilitate learning the requirements of API users. However, the unstructured nature of posts and the abundance of non-issue posts make the task of detecting SO posts concerning API issues difficult and challenging. In this paper, we first develop a supervised learning approach using a Conditional Random Field (CRF), a statistical modeling method, to identify API issue-related sentences. We use the above information together with different features of posts and experience of users to build a technique, called CAPS, that can classify SO posts concerning API issues. Evaluation of CAPS using carefully curated SO posts on three popular API types reveals that the technique outperforms all three baseline approaches we consider in this study. We also conduct studies to test the generalizability of CAPS results and to understand the effects of different sources of information on it.