Columbus - reverse engineering tool and schema for C++

Ferenć, Rudolf; Beszedes, Arpad; Tarkiainen, Mikko; Gyimóthy, Tibor

doi:10.1109/icsm.2002.1167764

Cited by 135 publications

(118 citation statements)

References 18 publications

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“…Other heavyweight C/C++ analyzers e.g. Columbus [12] or Clang [9] have the same issues. We also considered using lightweight C/C++ analyzers, e.g.…”

Section: Static Analysis: Ease Of Use Considerationsmentioning

confidence: 99%

“…SolidFX scales to millions of lines of code, covers several dialects (e.g. gcc, C89/99, ANSI C++), handles incorrect and incomplete code, has a preprocessor, and integrates with the gcc and Visual C++ build systems via compiler wrapping [12]. Still, certain options such as platform defines and headers cannot be inferred from build systems and must be manually specified.…”

Section: Static Analysis: Ease Of Use Considerationsmentioning

confidence: 99%

“…Static analyzers can be divided into lightweight ones, which use a subset of the target language grammar and semantics and trade fact completeness and accuracy for speed and simplicity; and heavyweight ones, which do full syntactic and semantic analysis at higher cost. Well-known static analyzers include LLVM [9], ROSE [10], Cppx [11], Columbus [12], Eclipse CDT [13], and Elsa [14] (for C/C++), Recoder [15] (for Java), and ASF+SDF (a metaframework with language-specific front-ends) [16]. Metric tools include CodeCrawler [17], Understand [18], and Visual Studio Team System (VSTS).…”

Section: Related Workmentioning

confidence: 99%

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The Solid* toolset for software visual analytics of program structure and metrics comprehension: From research prototype to product

Reniers

Voinea

Ersoy

et al. 2014

Science of Computer Programming

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Software visual analytics (SVA) tools combine static program analysis and fact extraction with information visualization to support program comprehension. However, building efficient and effective SVA tools is highly challenging, as it involves extensive software development in program analysis, graphics, information visualization, and interaction. We present a SVA toolset for software maintenance, and detail two of its components which target software structure, metrics and code duplication. We illustrate the toolset's usage for constructing software visualizations with examples in education, research, and industrial contexts. We discuss the design evolution from research prototypes to integrated, scalable, and easy-to-use products, and present several guidelines for the development of efficient and effective SVA solutions.

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“…Other heavyweight C/C++ analyzers e.g. Columbus [12] or Clang [9] have the same issues. We also considered using lightweight C/C++ analyzers, e.g.…”

Section: Static Analysis: Ease Of Use Considerationsmentioning

confidence: 99%

Section: Static Analysis: Ease Of Use Considerationsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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The Solid* toolset for software visual analytics of program structure and metrics comprehension: From research prototype to product

Reniers

Voinea

Ersoy

et al. 2014

Science of Computer Programming

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“…Columbus [38] is offering parsing capabilities from C/C++ source code and allows serializing the obtained information using different formats (e.g., XML, UML XMI). JaMoPP [39] or SpoonEMF [40] are providing alternative (but complete) Java metamodels and corresponding model discovery features from Java source code.…”

Section: Specific Reverse Engineering Solutionsmentioning

confidence: 99%

A Model Driven Reverse Engineering Framework for Extracting Business Rules Out of a Java Application

Cosentino

Cabot

Albert

et al. 2012

Rules on the Web: Research and Applications

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Context: Most companies, independently of their size and activity type, are facing the problem of managing, maintaining and/or replacing (part of) their existing software systems. These legacy systems are often large applications playing a critical role in the company's information system and with a non-negligible impact on its daily operations. Improving their comprehension (e.g., architecture, features, enforced rules, handled data) is a key point when dealing with their evolution/modernization.Objective: The process of obtaining useful higher-level representations of (legacy) systems is called reverse engineering (RE), and remains a complex goal to achieve. Socalled Model Driven Reverse Engineering (MDRE) has been proposed to enhance more traditional RE processes. However, generic and extensible MDRE solutions potentially addressing several kinds of scenarios relying on different legacy technologies are still missing or incomplete. This paper proposes to make a step in this direction.Method: MDRE is the application of Model Driven Engineering (MDE) principles and techniques to RE in order to generate relevant model-based views on legacy systems, thus facilitating their understanding and manipulation. In this context, MDRE is practically used in order to 1) discover initial models from the legacy artifacts composing a given system and 2) understand (process) these models to generate relevant views (i.e., derived models) on this system. Results: Capitalizing on the different MDRE practices and our previous experience (e.g., in real modernization projects), this paper introduces and details the MoDisco open source MDRE framework. It also presents the underlying MDRE global methodology and architecture accompanying this proposed tooling.Conclusion: MoDisco is intended to make easier the design and building of modelbased solutions dedicated to legacy systems RE. As an empirical evidence of its relevance and usability, we report on its successful application in real industrial projects and on the concrete experience we gained from that.

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“…task time, developers' experience, etc.). Process metrics are calculated from the Productivity Log Server, while the product metrics are obtained using the Columbus tool [6], which analyzes the source code retrieved from the version control server. The framework collects, processes, and aggregates the metrics which serve as input to the Weka [14] machine learning tool, which finally gives estimation models to predict the maintenance effort.…”

Section: The Frameworkmentioning

confidence: 99%

Adjusting effort estimation using micro-productivity profiles

Tóth¹,

Végh²,

Beszedes³

et al. 2013

Proc. Estonian Acad. Sci.

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We investigate a phenomenon we call micro-productivity decrease, which is expected to be found in most development or maintenance projects and has a specific profile that depends on the project, the development model and the team. Micro-productivity decrease refers to the observation that the cumulative effort to implement a series of changes is larger than the effort that would be needed if we made the same modification in only one step. The reason for the difference is that the same sections of code are usually modified more than once in the series of (sometimes imperfect) atomic changes. Hence, we suggest that effort estimation methods based on atomic change estimations should incorporate these profiles when being applied to larger modification tasks. We verify the concept on industrial development projects with our metrics-based machine learning models extended with statistical data. We show the calculated micro-productivity profile for these projects could be used for effort estimation of larger tasks with more accuracy than a naive atomic change oriented estimation.

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Columbus - reverse engineering tool and schema for C++

Abstract: One of the most critical issues in large-

Cited by 135 publications

References 18 publications

The Solid* toolset for software visual analytics of program structure and metrics comprehension: From research prototype to product

The Solid* toolset for software visual analytics of program structure and metrics comprehension: From research prototype to product

A Model Driven Reverse Engineering Framework for Extracting Business Rules Out of a Java Application

Adjusting effort estimation using micro-productivity profiles

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