Recommending pull request reviewers based on code changes

Ye, Xin; Zheng, Yongjie; Aljedaani, Wajdi; Mkaouer, Mohamed Wiem

doi:10.1007/s00500-020-05559-3

Cited by 18 publications

(10 citation statements)

References 21 publications

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“…The memory units enable the network to be aware of when to learn new information and when to forget old information (Ye et al. 2021 ). The basic LSTM structure is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

Aljedaani

Abuhaimed

Rustam

et al. 2022

Soc. Netw. Anal. Min.

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Introduction The development of COVID-19 vaccines has been a great relief in many countries that have been affected by the pandemic. As a result, many governments have made significant efforts to purchase and administer vaccines to their populations. However, accommodating such vaccines is typically confronted with people’s reluctance and fear. Like any other important event, COVID-19 vaccines have attracted people’s discussions on social media and impacted their opinions about vaccination. Objective The goal of this study is twofold: First, it conducts a sentiment analysis around COVID-19 vaccines by automatically analyzing Arabic users’ tweets. This analysis has been spread over time to better capture the changes in vaccine perceptions. This will provide us with some insights into the most popular and accepted vaccine(s) in the Arab countries, as well as the reasons behind people’s reluctance to take the vaccine. Second, it develops models to detect any vaccine-related tweets, to help with gathering all information related to people’s perception of the virus, and potentially detecting vaccine-related tweets that are not necessarily tagged with the virus’s main hashtags. Methods Arabic Tweets were collected by the authors, starting from January 1st, 2021, until April 20th, 2021. We deployed various Natural Language Processing (NLP) to distill our selected tweets. The curated dataset included in the analysis consisted of 1,098,376 unique tweets. To achieve the first goal, we designed state-of-the-art sentiment analysis techniques to extract knowledge related to the degree of acceptance of all existing vaccines and what are the main obstacles preventing the wide audience from accepting them. To achieve the second goal, we tackle the detection of vaccine-related tweets as a binary classification problem, where various Machine Learning (ML) models were designed to identify such tweets regardless of whether they use the vaccine hashtags or not. Results Generally, we found that the highest positive sentiments were registered for Pfizer-BioNTech, followed by Sinopharm-BIBP and Oxford-AstraZeneca. In addition, we found that 38% of the overall tweets showed negative sentiment, and only 12% had a positive sentiment. It is important to note that the majority of the sentiments vary between neutral and negative, showing the lack of conviction of the importance of vaccination among the large majority of tweeters. This paper extracts the top concerns raised by the tweets and advocates for taking them into account when advertising for the vaccination. Regarding the identification of vaccine-related tweets, the Logistic Regression model scored the highest accuracy of 0.82. Our findings are concluded with implications for public health authorities and the scholarly community to take into account to improve the vaccine’s acceptance.

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“…The memory units enable the network to be aware of when to learn new information and when to forget old information (Ye et al. 2021 ). The basic LSTM structure is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

Aljedaani

Abuhaimed

Rustam

et al. 2022

Soc. Netw. Anal. Min.

Self Cite

View full text Add to dashboard Cite

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“…Another interesting direction is to focus recommend reviewers that will ensure code base knowledge distribution [86,176,207]. Finally, some studies have included balancing review workload as an objective [43,49,86,230] In relation to how the predictors are used to recommend code reviewers, many employ traditional approaches (e.g., cosine similarity), while some use machine learning techniques, such as Random Forest [92], Naive Bayes [92,235], Support Vector Machines [144,276], Collaborative Filtering [87,230], Deep Neural Networks [222,274], or model reviewer recommendation as an optimization problem [43,86,187,207,211].…”

Section: Mcr Themes and Contributionsmentioning

confidence: 99%

Modern Code Reviews—Survey of Literature and Practice

Badampudi

Unterkalmsteiner

Britto

2023

ACM Trans. Softw. Eng. Methodol.

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Background: Modern Code Review (MCR) is a lightweight alternative to traditional code inspections. While secondary studies on MCR exist; it is unknown whether the research community has targeted themes that practitioners consider important. Objectives: The objectives are to provide an overview of MCR research, analyze the practitioners’ opinions on the importance of MCR research, investigate the alignment between research and practice, and propose future MCR research avenues. Method: We conducted a systematic mapping study to survey state-of-the-art until and including 2021, employed the Q-Methodology to analyze the practitioners’ perception of the relevance of MCR research, and analyzed the primary studies’ research impact. Results: We analyzed 244 primary studies, resulting in five themes. As a result of the 1300 survey data points, we found that the respondents are positive about research investigating the impact of MCR on product quality and MCR process properties. In contrast, they are negative about human factor- and support systems-related research. Conclusion: These results indicate a misalignment between the state-of-the-art and the themes deemed important by most survey respondents. Researchers should focus on solutions that can improve the state of MCR practice. We provide an MCR research agenda, which can potentially increase the impact of MCR research.

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“…Our approach uses pull requests to improve the SZZ algorithm. Regarding pull requests, data generated by pull-based workflows is already used by researchers to support decision making (e.g., [38], [39]) and to study pull request characteristics (e.g., [40]- [42]). For example, they find that the acceptance of pull requests is not directly influenced by the quality of the proposed code [40].…”

Section: Related Workmentioning

confidence: 99%

PR-SZZ: How pull requests can support the tracing of defects in software repositories

Peter

Pretschner

2022

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

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The SZZ algorithm represents a standard way to identify bug fixing commits as well as inducing counterparts. It forms the basis for data sets used in numerous empirical studies. Since its creation, multiple extensions have been proposed to enhance its performance. For historical reasons, related work relies on commit messages to map bug tickets to possibly related code with no additional data used to trace inducing commits from these fixes. Therefore, we present an updated version of SZZ utilizing pull requests, which are widely adopted today. We evaluate our approach in comparison to existing SZZ variants by conducting experiments and analyzing the usage of pull requests, inner commits, and merge strategies. We base our results on 6 open-source projects with more than 50k commits and 35k pull requests. With respect to bug fixing commits, on average 18% of bug tickets can be additionally mapped to a fixing commit, resulting in an overall F-score of 0.75, an improvement of 40 percentage points. By selecting an inducing commit, we manage to reduce the false-positives and increase precision by on average 16 percentage points in comparison to existing approaches.

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Recommending pull request reviewers based on code changes

Cited by 18 publications

References 21 publications

Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

Modern Code Reviews—Survey of Literature and Practice

PR-SZZ: How pull requests can support the tracing of defects in software repositories

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