Anomaly detection is nowadays a key data mining task. Anomaly detection methods generally look for patterns of ”normal” profile and then identify data points that do not match that profile. One outstanding method, Isolation Forest, showed high detection performance and computational efficiency. This method does not profile normal instances. Instead, it seeks to separate ”abnormal” data points that are ”few and different”, from the others. This method builds a forest of trees to define a model for classifying data points as normal or abnormal. Building trees involve recursive random partitioning of data. Instances resulting in shorter path lengths are considered anomalies because they are located in sparse regions whereas instances ending deeper in tree path are normal points. Most outlier detection approaches, including Isolation Forest, have been used on precise, crisp data. However, in real life, data may be imprecise, incomplete, ambiguous, or uncertain. In this paper, we propose a new approach, called Fuzzy Isolation Forest, which adapts the Isolation Forest method to fuzzy data to handle the ”vagueness” of data. We use a fuzzy partitioning strategy based on the alpha-cut approach in which data instances will be assigned to tree nodes by comparing their membership value to an alpha-cut threshold. Fuzzy Isolation Forest is as efficient as Isolation Forest in detecting anomalies on fuzzy data without significantly increasing processing time.

The adoption of refactoring techniques for continuous integration received much less attention from the research community comparing to root-canal refactoring to fix the quality issues in the whole system. Several recent empirical studies show that developers, in practice, are applying refactoring incrementally when they are fixing bugs or adding new features. There is an urgent need for refactoring tools that can support continuous integration and some recent development processes such as DevOps that are based on rapid releases. Furthermore, several studies show that manual refactoring is expensive and existing automated refactoring tools are challenging to configure and integrate into the development pipelines with significant disruption cost. In this paper, we propose, for the first time, an intelligent software refactoring bot, called RefBot. Integrated into the version control system (e.g. GitHub), our bot continuously monitors the software repository, and it is triggered by any "open" or "merge" action on pull requests. The bot analyzes the files changed during that pull request to identify refactoring opportunities using a set of quality attributes then it will find the best sequence of refactorings to fix the quality issues if any. The bot recommends all these refactorings through an automatically generated pull-request. The developer can review the recommendations and their impacts in a detailed report and select the code changes that he wants to keep or ignore. After this review, the developer can close and approve the merge of the bot's pull request. We quantitatively and qualitatively evaluated the performance and effectiveness of RefBot by a survey conducted with experienced developers who used the bot on both open source and industry projects.

The documentation of code changes is significantly important but developers ignore it, most of the time, due to the pressure of the deadlines. While developers may document the most important features modification or bugs fixing, recent empirical studies show that the documentation of quality improvements and/or refactoring is often omitted or not accurately described. However, the automated or semi-automated documentation of refactorings has not been yet explored despite the extensive work on the remaining steps of refactoring including the detection, prioritization and recommendation. In this paper, we propose a semi-automated refactoring documentation bot that helps developers to interactively check and validate the documentation of the refactorings and/or quality improvements at the file level for each opened pull-request before being reviewed or merged to the master. The bot starts by checking the pullrequest if there are significant quality changes and refactorings at the file level and whether they are documented by the developer. Then, it checks the validity of the developers description of the refactorings, if any. Based on that analysis, the documentation bot will recommend a message to document the refactorings, their locations and the quality improvement for that pull-request when missing information is found. Then, the developer can modify his pull request description by interacting with the bot to accept/modify/reject part of the proposed documentation. Since refactoring do not happen in isolation most of the time, the bot is documenting the impact of a sequence of refactorings, in a pull-request, on quality and not each refactoring in isolation. We conducted a human survey with 14 active developers to manually evaluate the relevance and the correctness of our tool on different pull requests of 5 open source projects and one industrial system. The results show that the participants found that our bot facilitates the documentation of their quality-related changes and refactorings.