A change-aware development environment by recording editing operations of source code

Omori, Takayuki; Maruyama, Katsuhisa

doi:10.1145/1370750.1370758

Cited by 39 publications

(39 citation statements)

References 11 publications

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“…A program comprehension tool could exploit such information. Tools that record fine-grained code changes and replay them [8]- [12] are typical examples of such plug-ins that have recently become feasible.…”

Section: Replaying Code Changesmentioning

confidence: 99%

“…In our previous work [12], we proposed OperationRecorder, an Eclipse plug-in that automatically records fine-grained changes (i.e., edit operations, not AST change operations) by continuously tracking the code edits performed on Eclipse's Java editor. Moreover, we presented OperationReplayer [15], an Eclipse plug-in that allows programmers to explore the source code restored by chronologically accumulating the recorded operations.…”

Section: Toolsmentioning

confidence: 99%

“…We adopt as a recording tool, OperationRecorder [12] † , which can automatically the record edit operations that affect the code in Eclipse's Java editor. The operations include manual typing (insertion, deletion, and replacement of text), editing by a clipboard (copying, cutting, and pasting text), undo/redo actions, and code changes by automatic transformation (code completion, quick fix, formatting, and refactoring).…”

Section: Recording and Replaying Edit Operationsmentioning

confidence: 99%

“…As a result, it will facilitate programmers' tasks of reusing or modifying existing code in To assist code history exploration, change-based support has recently become available [6], [7]. For example, SpyWare [8], Syde [9], Fluorite [10], CodingTracker [11], and OperationRecorder [12] are embedded into modern integrated development environments (IDEs) and capture all of the fine-grained code changes performed on the editors provided by their respective IDEs. In addition, some of these recording tools collaborate with tools that visualize, filter, and/or replay recorded code changes [13]- [16].…”

Section: Introductionmentioning

confidence: 99%

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Slicing Fine-Grained Code Change History

Maruyama

Omori

Hayashi

2016

IEICE Trans. Inf. & Syst.

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SUMMARYChange-aware development environments can automatically record fine-grained code changes on a program and allow programmers to replay the recorded changes in chronological order. However, since they do not always need to replay all the code changes to investigate how a particular entity of the program has been changed, they often eliminate several code changes of no interest by manually skipping them in replaying. This skipping action is an obstacle that makes many programmers hesitate when they use existing replaying tools. This paper proposes a slicing mechanism that automatically removes manually skipped code changes from the whole history of past code changes and extracts only those necessary to build a particular class member of a Java program. In this mechanism, fine-grained code changes are represented by edit operations recorded on the source code of a program and dependencies among edit operations are formalized. The paper also presents a running tool that slices the operation history and replays its resulting slices. With this tool, programmers can avoid replaying nonessential edit operations for the construction of class members that they want to understand. Experimental results show that the tool offered improvements over conventional replaying tools with respect to the reduction of the number of edit operations needed to be examined and over history filtering tools with respect to the accuracy of edit operations to be replayed.

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Section: Replaying Code Changesmentioning

confidence: 99%

Section: Toolsmentioning

confidence: 99%

Section: Recording and Replaying Edit Operationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Slicing Fine-Grained Code Change History

Maruyama

Omori

Hayashi

2016

IEICE Trans. Inf. & Syst.

Self Cite

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“…Omori and Maruyama [37,38] developed a similar fine-grained operation recorder and replayer for the Eclipse IDE. In contrast to CodingTracker, their tool does not infer AST node operations but rather associates code edits with AST nodes, to which they might belong.…”

Section: Tools For Fine-grained Analysis Of Code Evolutionmentioning

confidence: 99%

Is It Dangerous to Use Version Control Histories to Study Source Code Evolution?

Negara

Vakilian

Chen

et al. 2012

ECOOP 2012 – Object-Oriented Programming

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Abstract. Researchers use file-based Version Control System (VCS) as the primary source of code evolution data. VCSs are widely used by developers, thus, researchers get easy access to historical data of many projects. Although it is convenient, research based on VCS data is incomplete and imprecise. Moreover, answering questions that correlate code changes with other activities (e.g., test runs, refactoring) is impossible. Our tool, CodingTracker, non-intrusively records fine-grained and diverse data during code development. CodingTracker collected data from 24 developers: 1,652 hours of development, 23,002 committed files, and 314,085 testcase runs. This allows us to answer: How much code evolution data is not stored in VCS? How much do developers intersperse refactorings and edits in the same commit? How frequently do developers fix failing tests by changing the test itself? How many changes are committed to VCS without being tested? What is the temporal and spacial locality of changes?

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srcDiff: A syntactic differencing approach to improve the understandability of deltas

Decker

Collard

Volkert

et al. 2019

J Software Evolu Process

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An efficient and scalable rule‐based syntactic differencing approach is presented. The tool srcDiff is built upon the srcML infrastructure. srcML adds abstract syntactic information into the code via an XML format. A syntactic difference of srcML documents is then taken. During this process, the differences are further refined using a set of rules that model typical editing patterns of source code by developers. Thus, the resulting deltas model edits that are programmer centric versus a purely syntactic tree edit view. Other syntactic differencing approaches focus on obtaining an optimal tree edit distance with the assumption that this will produce an accurate difference. While this may work well for small or simple changes, the differences quickly become unreadable for more complex changes. By contrast, the approach presented here purposely deviates from an optimal tree edit difference in order to create a delta that is both easier to understand and better models changes between the original and modified. To evaluate the approach, a comparison user study against a state‐of‐the‐art syntactic differencing approach and two line‐based differencing tools is conducted as an online within‐participant study with about 70 subjects on 14 sample changes. The results provide support that the rule‐based syntactic differencing produces more accurate and understandable deltas.

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A change-aware development environment by recording editing operations of source code

Cited by 39 publications

References 11 publications

Slicing Fine-Grained Code Change History

Slicing Fine-Grained Code Change History

Is It Dangerous to Use Version Control Histories to Study Source Code Evolution?

srcDiff: A syntactic differencing approach to improve the understandability of deltas

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