Knowledge transfer is one of the main goals of modern code review, as shown by several studies that surveyed and interviewed developers. While knowledge transfer is a clear expectation of the code review process, there are no analytical studies using data mined from software repositories to assess the effectiveness of code review in “training” developers and improve their skills over time. We present a mining-based study investigating how and whether the code review process helps developers to improve their contributions to open source projects over time. We analyze 32,062 peer-reviewed pull requests (PRs) made across 4,981 GitHub repositories by 728 developers who created their GitHub account in 2015. We assume that PRs performed in the past by a developer D that have been subject to a code review process have “transferred knowledge” to D. Then, we verify if over time (i.e., when more and more reviewed PRs are made by D), the quality of the contributions made by D to open source projects increases (as assessed by proxies we defined, such as the acceptance of PRs, or the polarity of the sentiment in the review comments left for the submitted PRs). With the above measures, we were unable to capture the positive impact played by the code review process on the quality of developers’ contributions. This might be due to several factors, including the choices we made in our experimental design.Additional investigations are needed to confirm or contradict such a negative result.
Tue 14 JulDisplayed time zone: (UTC) Coordinated Universal Time change
08:30 - 09:30 | |||
08:30 12mPaper | A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning Research Fang Liu Peking University, Ge Li Peking University, Bolin Wei Peking University, Xin Xia Monash University, Zhiyi Fu Peking University, Zhi Jin Peking University Pre-print Media Attached | ||
08:42 12mPaper | Knowledge Transfer in Modern Code Review Research Maria Caulo University of Basilicata, Bin Lin Università della Svizzera italiana (USI), Gabriele Bavota Università della Svizzera italiana, Giuseppe Scanniello University of Basilicata, Michele Lanza Universita della Svizzera italiana (USI) Pre-print Media Attached | ||
08:54 12mPaper | How are Deep Learning Models Similar? An Empirical Study on Clone Analysis of Deep Learning Software Research Xiongfei Wu University of Science and Technology of China, Liangyu Qin University of Science and Technology of China, Bing Yu Kyushu University, Xiaofei Xie Nanyang Technological University, Lei Ma Kyushu University, Yinxing Xue , Yang Liu Nanyang Technological University, Singapore, Jianjun Zhao Kyushu University Media Attached | ||
09:06 12mPaper | Unified Configuration Setting Access in Configuration Management Systems Research Markus Raab Vienna University of Technology, Austria, Bernhard Denner Thales, Stefan Hanenberg University of Duisburg-Essen, Jürgen Cito MIT Media Attached | ||
09:18 12mPaper | Inheritance software metrics on smart contracts ERA Ashish Rajendra Sai University of Limerick, Conor Holmes University of Limerick, Jim Buckley Lero - The Irish Software Research Centre and University of Limerick, Andrew LeGear Horizon Globex Media Attached | ||