The Order of Things: How developers sort fields and methods

Biegel, Benjamin; Beck, Fabian; Hornig, Willi; Diehl, Stephan

doi:10.1109/icsm.2012.6405258

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Therefore, we looked for empirical results on the order of methods and fields in software systems. We found two: Biegel et al observed that in many cases, the code adheres to the structure specified in the Java Code Conventions published by Sun/Oracle, and that a clustering by visibility is also quite common [41]. They could also observe semantic clustering (by common key terms), whereas alphabetic order was rare.…”

Section: Empirical Findings On Real-world Code Structurementioning

confidence: 66%

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: Empirical Findings On Real-world Code Structurementioning

confidence: 66%

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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“…They observe among other things a tendency that putting methods related by call-flow together increases efficiency of understanding, especially for less experienced participants, but their results did not reach statistical significance (Geffen and Maoz, 2016). Another study of code ordering, but without the inclusion of a controlled experiment, has been performed by Biegel et al (2012). Both of these studies are rather exploratory and lack our theoretical underpinning regarding optimal code ordering.…”

Section: Ordering Of Code Change Parts For Review Reading Techniquesmentioning

confidence: 99%

Associating working memory capacity and code change ordering with code review performance

2019

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Change-based code review is a software quality assurance technique that is widely used in practice. Therefore, better understanding what influences performance in code review and finding ways to improve it can have a large impact. In this study, we examine the association of working memory capacity and cognitive load to code review performance and we test the predictions of a recent theory regarding improved code review efficiency with certain code change part orders. We perform a confirmatory experiment with 50 participants, mostly professional software developers. The participants performed code reviews on one small and two larger code changes from an open source software system to which we had seeded additional defects. We measured their efficiency and effectiveness in defect detection, their working memory capacity, and several potential confounding factors. We find that there is a moderate association between working memory capacity and the effectiveness of finding delocalized defects, influenced by other factors, whereas the association with other defect types is almost non-existing. We also confirm that the effectiveness of reviews is significantly larger for small code changes. We observe a tendency that the order of presenting the code changes influences the efficiency of code review, but cannot conclude this reliably.

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“…This may give theoretical value to the TDR practice. Code ordering and its relation to understanding, yet without explicit reference to tests or reviews, has also been the subject of studies [25], [12].…”

Section: Related Workmentioning

confidence: 99%

Test-Driven Code Review: An Empirical Study

Spadini

Palomba

Baum

et al. 2019

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

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Test-Driven Code Review (TDR) is a code review practice in which a reviewer inspects a patch by examining the changed test code before the changed production code. Although this practice has been mentioned positively by practitioners in informal literature and interviews, there is no systematic knowledge of its effects, prevalence, problems, and advantages. In this paper, we aim at empirically understanding whether this practice has an effect on code review effectiveness and how developers' perceive TDR. We conduct (i) a controlled experiment with 93 developers that perform more than 150 reviews, and (ii) 9 semi-structured interviews and a survey with 103 respondents to gather information on how TDR is perceived. Key results from the experiment show that developers adopting TDR find the same proportion of defects in production code, but more in test code, at the expenses of fewer maintainability issues in production code. Furthermore, we found that most developers prefer to review production code as they deem it more critical and tests should follow from it. Moreover, general poor test code quality and no tool support hinder the adoption of TDR. Public preprint: [

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The Order of Things: How developers sort fields and methods

Cited by 4 publications

References 10 publications

On the Optimal Order of Reading Source Code Changes for Review

On the Optimal Order of Reading Source Code Changes for Review

Associating working memory capacity and code change ordering with code review performance

Test-Driven Code Review: An Empirical Study

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