srcML: An Infrastructure for the Exploration, Analysis, and Manipulation of Source Code: A Tool Demonstration

Collard, Michael L.; Decker, Michael John; Maletic, Jonathan I.

doi:10.1109/icsm.2013.85

Cited by 103 publications

(40 citation statements)

References 16 publications

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“…We implemented a tool to apply the restructurings solely for this evaluation and to address RQ3. The tool is built using the srcML (http://www.srcML.org) infrastructure and libxml2 (http://xmlsoft.org). The srcML format is used for the representation of source code to support transformation via normalizing restructurings.…”

Section: Discussionmentioning

confidence: 99%

Simplifying the construction of source code transformations via automatic syntactic restructurings

Newman

Bartman

Collard

et al. 2017

J Software Evolu Process

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A set of restructurings to systematically normalize selective syntax in C++ is presented. The objective is to convert variations in syntax of specific portions of code into a single form to simplify the construction of large, complex program transformation rules. Current approaches to constructing transformations require developers to account for a large number of syntactic cases, many of which are syntactically different but semantically equivalent. The work identifies classes of such syntactic variations and presents normalizing restructurings to simplify each variation to a single, consistent syntactic form. The normalizing restructurings for C++ are presented and applied to two open source systems for evaluation. The evaluation uses the system's test cases to validate that the normalizing restructurings do not affect the systems' tested behavior. In addition, a set of example transformations that benefit from the prior application of normalizing restructurings are presented along with a small survey to assess the effect of the readability of the resultant code.

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Section: Discussionmentioning

confidence: 99%

Simplifying the construction of source code transformations via automatic syntactic restructurings

Newman

Bartman

Collard

et al. 2017

J Software Evolu Process

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“…The current implementation of srcDiff is a command-line tool (Windows, Linux, and Mac) in C++ using the srcML infrastructure. 17 The tool supports several different outputs: srcDiff (XML) for analysis or generation of personal visual outputs and side-by-side views and unified views for humans (in both text and HTML). As a command-line tool, the approach is easily integrated into software development environments.…”

Section: Methodsmentioning

confidence: 99%

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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“…Otherwise, its children are appended on the work list for later consideration. The T-ORBS implementation we used employs SrcML [19] to produce an XML tree from a program. It is important to note that we modified the original T-ORBS algorithm to attempt to delete only those syntactic elements that are considered by MOAD; in this case statements.…”

Section: Observation-based Slicing (Orbs)mentioning

confidence: 99%

“…In the initial experiments, the units considered by MOAD are programming language statements. We use SrcML (version 0.9.5) [19], an XML-based multi-language parsing tool, to identify statements. SrcML enables our approach to be applied to any programming language, including multi-lingual programs, that SrcML can process.…”

Section: Configurationmentioning

confidence: 99%

MOAD: Modeling Observation-Based Approximate Dependency

Lee

Binkley

Feldt

et al. 2019

2019 19th International Working Conference on Source Code Analysis and Manipulation (SCAM)

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While dependency analysis is foundational to many applications of program analysis, the static nature of many existing techniques presents challenges such as limited scalability and inability to cope with multilingual systems. We present a novel dependency analysis technique that aims to approximate program dependency from a relatively small number of perturbed executions. Our technique, called MOAD (Modeling Observation-based Approximate Dependency), reformulates program dependency as the likelihood that one program element is dependent on another, instead of a more classical Boolean relationship. MOAD generates a set of program variants by deleting parts of the source code, and executes them while observing the impacts of the deletions on various program points. From these observations, MOAD infers a model of program dependency that captures the dependency relationship between the modification and observation points. While MOAD is a purely dynamic dependency analysis technique similar to Observation Based Slicing (ORBS), it does not require iterative deletions. Rather, MOAD makes a much smaller number of multiple, independent observations in parallel and infers dependency relationships for multiple program elements simultaneously, significantly reducing the cost of dynamic dependency analysis. We evaluate MOAD by instantiating program slices from the obtained probabilistic dependency model. Compared to ORBS, MOAD's model construction requires only 18.7% of the observations used by ORBS, while its slices are only 16% larger than the corresponding ORBS slice, on average.

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srcML: An Infrastructure for the Exploration, Analysis, and Manipulation of Source Code: A Tool Demonstration

Cited by 103 publications

References 16 publications

Simplifying the construction of source code transformations via automatic syntactic restructurings

Simplifying the construction of source code transformations via automatic syntactic restructurings

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

MOAD: Modeling Observation-Based Approximate Dependency

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