Program Analysis Scenarios in Rascal

Hills, Mark; Klint, Paul; Vinju, Jurgen

doi:10.1007/978-3-642-34005-5_2

Cited by 4 publications

(5 citation statements)

References 37 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Heavy-weight models similar to FAMIX, such as KDM [4] or ASTM [3] as well as general-purpose models such as RSF [34] or GXL [68] model not only of the kinds of nodes, but also their relationships and structure explicitely. The same is true for M 3 [32], which is specifically designed for use with Rascal Metaprogramming Language [29] and which also includes non-code concepts such as physical and logical source locations.…”

Section: B Multi-language Analysismentioning

confidence: 99%

Reducing redundancies in multi-revision code analysis

Alexandru

Panichella

Gall

2017

2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)

View full text Add to dashboard Cite

Software engineering research often requires analyzing multiple revisions of several software projects, be it to make and test predictions or to observe and identify patterns in how software evolves. However, code analysis tools are almost exclusively designed for the analysis of one specific version of the code, and the time and resources requirements grow linearly with each additional revision to be analyzed. Thus, code studies often observe a relatively small number of revisions and projects. Furthermore, each programming ecosystem provides dedicated tools, hence researchers typically only analyze code of one language, even when researching topics that should generalize to other ecosystems. To alleviate these issues, frameworks and models have been developed to combine analysis tools or automate the analysis of multiple revisions, but little research has gone into actually removing redundancies in multi-revision, multi-language code analysis. We present a novel end-to-end approach that systematically avoids redundancies every step of the way: when reading sources from version control, during parsing, in the internal code representation, and during the actual analysis. We evaluate our open-source implementation, LISA, on the full history of 300 projects, written in 3 different programming languages, computing basic code metrics for over 1.1 million program revisions. When analyzing many revisions, LISA requires less than a second on average to compute basic code metrics for all files in a single revision, even for projects consisting of millions of lines of code.

show abstract

Section: B Multi-language Analysismentioning

confidence: 99%

Reducing redundancies in multi-revision code analysis

Alexandru

Panichella

Gall

2017

2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)

View full text Add to dashboard Cite

show abstract

“…Heavyweight models similar to FAMIX, such as KDM [4] or ASTM [3] as well as general-purpose models such as RSF [45] or GXL [86] model not only the kinds of nodes, but also their relationships and structure explicitly. The same is true for M 3 [41], which is specifically designed for use with Rascal Metaprogramming Language [40] and which also includes noncode concepts such as physical and logical source locations. In addition, recent work proposed rather different approaches focused on the execution of specific static analyses on multi-language programs such as the detection of software vulnerabilities [38] and changes in software licenses [21]).…”

Section: Multi-language Analysismentioning

confidence: 99%

Redundancy-free analysis of multi-revision software artifacts

Alexandru¹,

Panichella²,

Proksch³

et al. 2018

Empir Software Eng

View full text Add to dashboard Cite

Researchers often analyze several revisions of a software project to obtain historical data about its evolution. For example, they statically analyze the source code and monitor the evolution of certain metrics over multiple revisions. The time and resource requirements for running these analyses often make it necessary to limit the number of analyzed revisions, e.g., by only selecting major revisions or by using a coarse-grained sampling strategy, which could remove significant details of the evolution. Most existing analysis techniques are not designed for the analysis of multi-revision artifacts and they treat each revision individually. However, the actual difference between two subsequent revisions is typically very small. Thus, tools tailored for the analysis of multiple revisions should only analyze these differences, thereby preventing re-computation and storage of redundant data, improving scalability and enabling the study of a larger number of revisions. In this work, we propose the Lean Language-Independent Software Analyzer (LISA), a generic framework for representing and analyzing multi-revisioned software artifacts. It employs a redundancy-free, multi-revision representation for artifacts and avoids re-computation by only analyzing changed artifact fragments across thousands of revisions. The evaluation of our approach consists of measuring the effect of each individual technique incorporated, an in-depth study of LISA resource requirements and a large-scale analysis over 7 million program revisions of 4,000 software projects written in four languages. We show that the time and space requirements for multi-revision analyses can be reduced by multiple orders of magnitude, when compared to traditional, sequential approaches.

show abstract

“…Its goal is to be a language for the analysis, transformation, generation and visualization of source code, regardless of the kind or combination of programming language [10]. It is in active development by the SWAT group at CWI Amsterdam 6 and is the successor to the 5 http://www.rascal-mpl.org/ 6 http://www.cwi.nl/research-groups/software-analysis-and-transformation ASF and ASF+SDF Meta Environment 7 .…”

Section: Spoofax/impmentioning

confidence: 99%

“…The grammar can be specified in an EBNF-like form and various disambiguation information like associativity and precedence can be specified [10]. The generated parse tree consists of ADTs, which need to be specified previously, and is annotated with location information.…”

Section: Spoofax/impmentioning

confidence: 99%

See 1 more Smart Citation

An Evaluation of Domain-Specific Language Technologies for Code Generation

Schmitt

Kuckuk

Köstler

et al. 2014

2014 14th International Conference on Computational Science and Its Applications

View full text Add to dashboard Cite

Abstract-Software systems are becoming increasingly complex, requiring a deep knowledge to work and program with them. This is especially true for simulation frameworks used by scientists and engineers, but also applies to completely different domains such as mobile or web applications. To ease working with these systems, Domain-Specific Languages (DSLs) are a convenient way to enable domain experts describe settings and problems they want to solve using terms familiar to them. Building upon this specification in the DSL, a compiler transform this to the target software framework, e. g., runnable program code. To write such a compiler, a solid implementation framework is needed. In this paper, we propose criteria for the evaluation of textual programming language implementation frameworks to which we accordingly evaluate four technologies, namely Spoofax/IMP, Rascal MPL, a custom approach using C ++ and a custom approach using Scala.

show abstract

Program Analysis Scenarios in Rascal

Cited by 4 publications

References 37 publications

Reducing redundancies in multi-revision code analysis

Reducing redundancies in multi-revision code analysis

Redundancy-free analysis of multi-revision software artifacts

An Evaluation of Domain-Specific Language Technologies for Code Generation

Contact Info

Product

Resources

About