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Young Ninja Group (ages 3-5)

John Ayers
John Ayers

A Tangled Web Cblack Pdf 12

Growing along the edge of the shoreline where conditions are harshest, the red mangrove (Rhizophora mangle) is easily distinguished from other species by tangled, reddish prop roots. These prop roots originate from the trunk with roots growing downward from the branches. Extending three feet (1 m) or more above the surface of the soil, prop roots increase stability of the tree as well as oxygen supply to underground roots.

a tangled web cblack pdf 12

Qualitative studies reported that professional developers perceive tangled code changes as problematic and asked for tools to automatically decompose them (Tao et al., 2012; Barnett et al., 2015). Accordingly, change untangling mechanisms have been proposed (Tao & Kim, 2015; Dias et al., 2015; Barnett et al., 2015).

To this end, we designed a controlled experiment focusing on pull requests, a widespread approach to submit and review changes (Gousios et al., 2015). Our work investigates whether the results from Tao & Kim (2015) can be replicated, and extend the knowledge on the topic. With a Java system as a subject, we asked 28 software developers among professionals and graduate students to review a refactoring and a new feature (according to professional developers (Tao et al., 2012), these are the most difficult to review when tangled). We measure how the partitioning vs. non-partitioning of the changes impacts defects found, false positive issues, suggested improvements, time to review, and understanding the change rationale. We also perform qualitative observations on how subjects conduct the review and address defects or raise false positives, in the two scenarios.

Even if MCR is now a mainstream process, adopted in both open source and industrial projects, we found only two studies on change partitioning and its benefits for code review. The work by Barnett et al. (2015) analyzed the usefulness of an automatic technique for decomposing changesets. They found a positive association between change decomposition and the level of understanding of the changesets. According to their results, this would help time to review as the different contexts are separated. Tao & Kim (2015) proposed a heuristic-based approach to decompose changeset with multiple concepts. They conducted a user study with students investigating whether their untangling approach affected the time and the correctness in performing review-related tasks. Results were promising: Participants completed the tasks better with untangled changes in a similar amount of time. In spite of the innovative techniques they proposed to untangle code changes and in these promising results, the evaluation of effects of change decomposition was preliminary.

RQ1. Do tangled pull requests influence effectiveness (i.e., number of defects found), false positives, and suggested improvements of reviewers, when compared to untangled pull requests?

These changes can be inspected in the online appendix (di Biase et al., 2018) and have been chosen to meet the same criteria used by Herzig, Just & Zeller (2016) when choosing tangled changes. Changes proposed can be classified as refactoring and enhancement. Previous literature gave insight as to how these two kinds of changes, when tangled together, are the hardest to review (Tao et al., 2012). Although recent research proposed a theory for the optimal ordering of code changes in a review (Baum, Schneider & Bacchelli, 2017), we used the default ordering and presentation provided by GitHub, because it is the de-facto standard. Changesets were included in pull requests on private GitHub repositories so that participants performed the tasks in a real-world review environment. Pull requests had identical descriptions for both the control and the treatment, with no additional information except their descriptive title. While research showed that a short description may lead to poor review participation (Thongtanunam et al., 2017), this does not apply to our experiment as there is no interaction among subjects.

Result 2:Our experiment was not able to provide evidence for a difference in net review time between untangled pull requests (treatment) and the tangled one (control); this despite the additional overhead of dealing with two separate pull requests in the treatment group.

Result 3:Our experiment was not able to provide evidence of a difference in understanding the rationale of the changeset between the experimental groups. Subjects reviewing the untangled pull requests (treatment) recognize the benefits of untangled pull requests, as they evaluate the changeset as being (1) better divided according to a logical separation of concerns (Q8), (2) better structured (Q10), and (3) not spanning too many features (Q12).

Result 4:Our experiment revealed that review patterns for untangled pull requests (treatment) show more context-seeking steps, in which the participants open more referenced/related classes to review the changeset.

We involved 28 subjects, who performed a review of pull request(s) pertaining to (1) a refactoring and (2) the addition of a new feature in a Java system. The control group received a single pull request with both changes tangled together, while the treatment group received two pull requests (one per type of change). We compared control and treatment groups in terms of effectiveness (number of defects found), number of false positives (wrongly reported issues), number of suggested improvements, time to complete the review(s), and level of understanding the rationale of the change. Our investigation also involved a qualitative analysis of the review performed by subjects involved in our study.


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