A Faceted Classification Scheme for Change-Based Industrial Code Review Processes

Baum, Tobias; Liskin, Olga; Niklas, Kai; Schneider, Kurt

doi:10.1109/qrs.2016.19

Cited by 35 publications

(31 citation statements)

References 27 publications

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“…Many interview participants preferred to go top-down from caller to callee, but others also talked about going bottom-up from callee to caller. In contrast, the survey results support bottom-up (see The "code change" consists of all changes to source files performed in the "unit of work" [2] under review. This also includes auxiliary sources like test code, configuration files, etc.…”

Section: The Micro Structure: Relations Between Change Partsmentioning

confidence: 65%

“…The scope of our theory is change-based code review [2]. The theory has been developed based on code written in object-oriented languages; it possibly has to be adapted to be applicable to other programming paradigms.…”

Section: A Scope and Constructsmentioning

confidence: 99%

“…In recent years, its use in industry is converging towards 'change-based code review' [2], 1 a variant of code review embedded into the software development process such that the changes performed in some development task define the review scope. Change-based code review has gained a vast popularity [5], especially in the form of pullbased software development [6] as provided by GitHub [7], therefore ways to improve the effectiveness and efficiency of change-based code review can lead to a significant impact.…”

Section: Introductionmentioning

confidence: 99%

“…combine input from multiple data sources: (1) Log data from 292 tool-based review sessions in industry; (2) task-guided interviews with 12 professional developers; (3) related work from fields such as cognitive science and program comprehension; and (4) a task-based survey answered by 201 reviewers. We present our findings as a collection of 'principles' given in natural language and we formalize them into a theory 2 to improve their verifiability and utility for follow-up studies.…”

Section: Introductionmentioning

confidence: 99%

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On the Optimal Order of Reading Source Code Changes for Review

Baum

Schneider

Bacchelli

2017

2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Change-based code review, e.g., in the form of pull requests, is the dominant style of code review in practice. An important option to improve review's efficiency is cognitive support for the reviewer. Nevertheless, review tools present the change parts under review sorted in alphabetical order of file path, thus leaving the effort of understanding the construction, connections, and logic of the changes on the reviewer. This leads to the question: How should a code review tool order the parts of a code change to best support the reviewer? We answer this question with a middle-range theory, which we generated inductively in a mixed methods study, based on interviews, an online survey, and existing findings from related areas. Our results indicate that an optimal order is mainly an optimal grouping of the change parts by relatedness. We present our findings as a collection of principles and formalize them as a partial order relation among review orders. Abstract-Change-based code review, e.g., in the form of pull requests, is the dominant style of code review in practice. An important option to improve review's efficiency is cognitive support for the reviewer. Nevertheless, review tools present the change parts under review sorted in alphabetical order of file path, thus leaving the effort of understanding the construction, connections, and logic of the changes on the reviewer. This leads to the question: How should a code review tool order the parts of a code change to best support the reviewer? We answer this question with a middle-range theory, which we generated inductively in a mixed methods study, based on interviews, an online survey, and existing findings from related areas. Our results indicate that an optimal order is mainly an optimal grouping of the change parts by relatedness. We present our findings as a collection of principles and formalize them as a partial order relation among review orders.

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Section: The Micro Structure: Relations Between Change Partsmentioning

confidence: 65%

Section: A Scope and Constructsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Optimal Order of Reading Source Code Changes for Review

Baum

Schneider

Bacchelli

2017

2017 IEEE International Conference on Software Maintenance and Evolution (ICSME)

Self Cite

View full text Add to dashboard Cite

show abstract

“…This sparks discussions in development teams on the preferable way to perform reviews. To better understand the variations in review processes in practice, we performed in‐depth interviews in 19 software companies, ranging from small start‐ups and standard software producers to large in‐house IT departments . In these interviews, the software development professionals named several reasons why they believe their method of choice is more efficient: Pre‐commit reviews find defects before they impede other developers. Pre‐commit reviews might extend the cycle time of user stories, increasing the “work in progress” and consequently increasing task switch overhead. Post‐commit reviews support the early and often integration of changes, better suiting the mindset behind continuous integration and possibly reducing the risk of conflicts. …”

Section: Introductionmentioning

confidence: 99%

Comparing pre‐commit reviews and post‐commit reviews using process simulation

Baum

Kortum

Schneider

et al. 2017

J Software Evolu Process

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Code review in practice is often performed change‐based, ie, using the code changes belonging to a task to determine which code to review. Previous studies found that 2 variations of this process are used in industry: pre‐commit review and post‐commit review. The choice between these has implications not only for practitioners deciding on a code review process to use but also for the development of review tools and for experimentation with review processes. In some situations, a specific variant is clearly preferable due to the nature of the development process or team. In other situations, there are conflicting opinions. So we asked: Are there practically relevant performance differences between pre and post‐commit reviews? How are these differences influenced by contextual factors? To assess these questions, we designed a parametric discrete event simulation model of certain agile development processes. We validated this model with practitioner's feedback and in part also with empirical data from industry. Our analysis indicates that the best choice does depend on the context but also that there are many situations with no practically relevant difference between both choices. We identified the main influencing factors and underlying effects and condensed our findings into heuristic rules.

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Code Change Difference Analysis Tool Design and Implementation Based on Eclipse Plug-in

Gong

Yang

2021

Advances in Intelligent Systems and Computing

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A Faceted Classification Scheme for Change-Based Industrial Code Review Processes

Cited by 35 publications

References 27 publications

On the Optimal Order of Reading Source Code Changes for Review

On the Optimal Order of Reading Source Code Changes for Review

Comparing pre‐commit reviews and post‐commit reviews using process simulation

Code Change Difference Analysis Tool Design and Implementation Based on Eclipse Plug-in

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