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Abstract-Refactorings are behavior-preserving source code transformations. While tool support exists for (semi)automatically identifying refactoring solutions, applying or not a recommended refactoring is usually up to the software developers, who have to assess the impact that the transformation will have on their system. Evaluating the pros (e.g., the bad smell removal) and cons (e.g., side effects of the change) of a refactoring is far from trivial. We present RIPE (Refactoring Impact PrEdiction), a technique that estimates the impact of refactoring operations on source code quality metrics. RIPE supports 12 refactoring operations and 11 metrics and it can be used together with any refactoring recommendation tool. RIPE was used to estimate the impact on 8,103 metric values, for 504 refactorings from 15 open source systems. 38% of the estimates are correct, whereas the median deviation of the estimates from the actual values is 5% (with a 31% average).

A major problem with user-written bug reports, indicated by developers and documented by researchers, is the (lack of high) quality of the reported steps to reproduce the bugs. Low-quality steps to reproduce lead to excessive manual effort spent on bug triage and resolution. This paper proposes Euler, an approach that automatically identifies and assesses the quality of the steps to reproduce in a bug report, providing feedback to the reporters, which they can use to improve the bug report. The feedback provided by Euler was assessed by external evaluators and the results indicate that Euler correctly identified 98% of the existing steps to reproduce and 58% of the missing ones, while 73% of its quality annotations are correct. CCS CONCEPTS• Software and its engineering → Maintaining software.

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benet to mobile developers. Unfortunately, automatically analyzing screen recordings presents serious challenges, due to their graphical nature, compared to other types of (textual) artifacts. To address these challenges, this paper introduces V2S, a lightweight, automated approach for translating video recordings of Android app usages into replayable scenarios. V2S is based primarily on computer vision techniques and adapts recent solutions for object detection and image classication to detect and classify user actions captured in a video, and convert these into a replayable test scenario. We performed an extensive evaluation of V2S involving 175 videos depicting 3,534 GUI-based actions collected from users exercising features and reproducing bugs from over 80 popular Android apps. Our results illustrate that V2S can accurately replay scenarios from screen recordings, and is capable of reproducing ⇡ 89% of our collected videos with minimal overhead. A case study with three industrial partners illustrates the potential usefulness of V2S from the viewpoint of developers. CCS CONCEPTS • Software and its engineering → Software maintenance tools; Software verication and validation; Application specic development environments.

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This paper introduces Bee, a tool that automatically analyzes userwritten bug reports and provides feedback to reporters and developers about the system's observed behavior (OB), expected behavior (EB), and the steps to reproduce the bug (S2R). Bee employs machine learning to (i) detect if an issue describes a bug, an enhancement, or a question; (ii) identify the structure of bug descriptions by automatically labeling the sentences that correspond to the OB, EB, or S2R; and (iii) detect when bug reports fail to provide these elements. Bee is integrated with GitHub and offers a public web API that researchers can use to investigate bug management tasks based on bug reports. We evaluated Bee's underlying models on more than 5k existing bug reports and found they can correctly detect OB, EB, and S2R sentences as well as missing information in bug reports. Bee is an open-source project that can be found at https://git.io/JfFnN. A screencast showing the full capabilities of Bee can be found at https://youtu.be/8pC48f_hClw. CCS CONCEPTS • Software and its engineering → Software maintenance tools.

When a bug manifests in a user-facing application, it is likely to be exposed through the graphical user interface (GUI). Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers. However, when such information is reported en masse, such as during crowd-sourced testing, managing these artifacts can be a time-consuming process. As the reporting of screen-recordings in particular becomes more popular, developers are likely to face challenges related to manually identifying videos that depict duplicate bugs. Due to their graphical nature, screen-recordings present challenges for automated analysis that preclude the use of current duplicate bug report detection techniques. To overcome these challenges and aid developers in this task, this paper presents TA N G O , a duplicate detection technique that operates purely on video-based bug reports by leveraging both visual and textual information. TANGO combines tailored computer vision techniques, optical character recognition, and text retrieval. We evaluated multiple configurations of Ta n g o in a comprehensive empirical evaluation on 4,860 duplicate detection tasks that involved a total of 180 screenrecordings from six Android apps. Additionally, we conducted a user study investigating the effort required for developers to manually detect duplicate video-based bug reports and compared this to the effort required to use TA N G O . The results reveal that TA N G O 's optimal configuration is highly effective at detecting duplicate video-based bug reports, accurately ranking target duplicate videos in the top-2 returned results in 83% of the tasks. Additionally, our user study shows that, on average, TANGO can reduce developer effort by over 60%, illustrating its practicality.