A systematic literature review and taxonomy of modern code review

Davila, Nicole; Ingrid, Nunes

doi:10.26226/morressier.613b5419842293c031b5b639

Cited by 5 publications

(5 citation statements)

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“…Our work relates to three research areas: (i) DL techniques to automate software-related tasks, (ii) empirical studies on code review, and (iii) works providing recommendations on how to optimize the code review process and/or presenting techniques to partially automate it. Here we focus on the third research area, while for the first two we point the reader to the systematic literature reviews by Watson et al [49] (deep learning in software engineering) and by Davila and Nunes [20] (modern code review).…”

Section: Related Workmentioning

confidence: 99%

Using Pre-Trained Models to Boost Code Review Automation

Tufano¹,

Masiero²,

Mastropaolo³

et al. 2022

Preprint

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Code review is a practice widely adopted in open source and industrial projects. Given the non-negligible cost of such a process, researchers started investigating the possibility of automating specific code review tasks. We recently proposed Deep Learning (DL) models targeting the automation of two tasks: the first model takes as input a code submitted for review and implements in it changes likely to be recommended by a reviewer; the second takes as input the submitted code and a reviewer comment posted in natural language and automatically implements the change required by the reviewer. While the preliminary results we achieved are encouraging, both models had been tested in rather simple code review scenarios, substantially simplifying the targeted problem. This was also due to the choices we made when designing both the technique and the experiments. In this paper, we build on top of that work by demonstrating that a pre-trained Text-To-Text Transfer Transformer (T5) model can outperform previous DL models for automating code review tasks. Also, we conducted our experiments on a larger and more realistic (and challenging) dataset of code review activities.

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Section: Related Workmentioning

confidence: 99%

Using Pre-Trained Models to Boost Code Review Automation

Tufano¹,

Masiero²,

Mastropaolo³

et al. 2022

Preprint

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“…A mapping study by Badampudi et al [2] covering research on modern code review 2005-2018 shows that process and tool improvements, impact and outcome, and reviewer selection are the top-3 research areas in the field during this period. A recent mapping study by Davila and Nunes [9] further proposes a taxonomy of the research on modern code review. In their taxonomy, the work presented here falls into two categories 1 : (1) foundational studies, i.e.…”

Section: Related Workmentioning

confidence: 99%

Understanding the Experience of Code Review: Misalignments, Attention, and Units of Analysis

Söderberg

Church

Börstler

et al. 2022

The International Conference on Evaluation and Assessment in Software Engineering 2022

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Code review is a common practice in software development and numerous studies have described different aspects of the process; its characteristics, the expectations on that process, issues around reviewer allocation, and more. However, one aspect that has not been studied to a large extent is the experience of the developers in the code review process. This is unfortunate given the significant amount of time that developers spend on this activity, where problems that degrade developers' experience on a daily basis can create work environment issues.In this paper, we present an extended analysis of an exploratory mixed-method study where we focus on developers' experience of code review. We use semi-structured interviews to gather data from two multi-national companies and conduct a follow-up survey. Our results suggest that developers are frequently bothered by misalignments in the code review tooling and process which is hindering them in carrying out their code review tasks effectively. We present an initial characterization of misalignments that may hamper the developer experience. Based on our findings, we propose directions for further exploration to improve the developer experience. CCS CONCEPTS• Software and its engineering → Software maintenance tools.

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“…Other studies on modern code review are discussed by Davila and Nunes [2021]. The authors performed a systematic review of 139 papers, where foundational studies, proposals, and evaluations from four digital libraries were analyzed.…”

Section: Code Reviewmentioning

confidence: 99%

RAID: Tool Support for Refactoring-Aware Code Reviews

Brito

Valente

2021

2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC)

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Agradeço aos meus familiares, amigos e professores! A presença de vocês foi indispensável para a realização desse sonho. Agradeço em especial:A Deus, por permitir a conclusão desta etapa.Aos meus familiares, pelo incentivo durante este curso de mestrado. Especialmente, aos meus pais Bernadete e José, e à minha irmã Aline, por todo o carinho, apoio e conselhos recebidos.Aos meus amigos, pela amizade, suporte e por todos os momentos incríveis que passamos juntos. Em especial aos meus amigos do Aserg, pela parceria, amizade e aprendizado.Aos meus companheiros de trabalho da Studio Sol, pelo companheirismo e contribuição no meu crescimento profissional e pessoal.Ao meu orientador, Prof. Marco Tulio Valente, pela paciência, confiança e todos os ensinamentos passados durante esse período. Aos meus professores, pelo incentivo e conhecimento compartilhado durante o mestrado, graduação e ensino básico.Aos membros da banca, Prof. Ingrid Nunes e Prof. André Hora, pela disponibilidade em participar deste trabalho.Ao DCC/UFMG, FAPEMIG e CNPq, pelo suporte financeiro e acadêmico.vi "Great things are done by a series of small things brought together."(Vincent Van Gogh) vii ResumoRevisão do código é uma importante prática no desenvolvimento de software moderno. Além de detectar falhas, revisão de código contribui para a qualidade do código e transferência de conhecimento. No entanto, revisão do código leva tempo e exige uma análise detalhada e demorada de diffs textuais. Particularmente, detectar refatorações durante as revisões não é uma tarefa trivial, uma vez que as refatorações não são representadas em diffs. Nesta dissertação, nós inicialmente estendemos RefDiff -uma ferramenta de detecção de refatoração multilinguagem -para identificar refatorações na linguagem de programação Go. Nossa extensão-chamada RefDiff4Go-detecta 13 tipos de refatoração e obteve uma precisão de 92% e um recall de 80% em uma avaliação com seis projetos de código aberto populares. Em seguida, como nossa principal contribuição, nós apresentamos RAID: uma ferramenta de diff inteligente que identifica atividades de refatoração e instrumenta os diffs textuais-particularmente, os diffs fornecidos pelo GitHub-com informações de atividades de refatoração. Nosso objetivo é aliviar o esforço cognitivo associado a revisões de código, detectando automaticamente as operações de refatoração incluídas nas solicitações de pull requests. Além de propor uma arquitetura para o RAID, implementamos um plug-in para o navegador Chrome que suporta nossa solução. Também avaliamos RAID em um experimento de campo por três meses, quando oito desenvolvedores profissionais usaram nossa ferramenta em quatro projetos Go. Concluímos que RAID reduz o esforço cognitivo necessário para detectar e revisar atividades de refatoração em diffs textuais. Particularmente, RAID também reduz o número de linhas necessárias para revisar tais operações. Por exemplo, o número médio de linhas a serem revisadas diminuiu de 14,5 para 2 linhas no caso de refatorações envolvendo movimentação e de 113 p...

show abstract

A systematic literature review and taxonomy of modern code review

Cited by 5 publications

References 0 publications

Using Pre-Trained Models to Boost Code Review Automation

Using Pre-Trained Models to Boost Code Review Automation

Understanding the Experience of Code Review: Misalignments, Attention, and Units of Analysis

RAID: Tool Support for Refactoring-Aware Code Reviews

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