Change Analysis with Evolizer and ChangeDistiller

Gall, Harald C.; Fluri, Beat; Pinzger, Martin

doi:10.1109/ms.2009.6

Cited by 96 publications

(68 citation statements)

References 12 publications

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“…These sets validated our findings, as reported in the previous sections, but also confirmed the known limitation of this and any SLR: the limitation due to circumscribing the review and thus missing interesting papers. For example, our discussions with the colleagues working on FAMIX pointed us to works in which FAMIX was used to model program metadata and related data, including, but not limited to: Evolizer to analyze source code and software project data (Gall et al 2009), ChEOPS to represent changes as first-class entities for change-oriented software engineering (Ebraert et al 2007), and Orion to model simultaneous versions in a software version repository ). We did not include these works in our review because they were beyond the borders of our review.…”

Section: Limitationsmentioning

confidence: 99%

ProMeTA: a taxonomy for program metamodels in program reverse engineering

2018

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To support program comprehension, maintenance, and evolution, metamodels are frequently used during program reverse engineering activities to describe and analyze constituents of a program and their relations. Reverse engineering tools often define their own metamodels according to the intended purposes and features. Although each metamodel has its own advantages, its limitations may be addressed by other metamodels. Existing works have evaluated and compared metamodels and tools, but none have considered all the possible characteristics and limitations to provide a comprehensive guideline for classifying, comparing, reusing, and extending program metamodels. To aid practitioners and researchers in classifying, comparing, reusing, and extending program metamodels and their corresponding reverse engineering tools according to the intended goals, we establish a conceptual framework with definitions of program metamodels and related concepts. We confirmed that any reverse engineering activity can be clearly described as a pattern based on the framework from the viewpoint of program metamodels. Then the framework is used (2018) 23:2323-2358 to provide a comprehensive taxonomy, named Program Metamodel TAxonomy (ProMeTA), which incorporates newly identified characteristics into those stated in previous works, which were identified via a systematic literature review (SLR) on program metamodels, while keeping the orthogonality of the entire taxonomy. Additionally, we validate the taxonomy in terms of its orthogonality and usefulness through the classification of popular metamodels.Empir Software Eng

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Section: Limitationsmentioning

confidence: 99%

ProMeTA: a taxonomy for program metamodels in program reverse engineering

2018

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“…We need four kinds of information to prepare the dataset for our experiments in Section III: (1) Source code dependency graph; (2) Centrality measures from social network analysis [12] based on the dependency graph; (3) object-oriented source code metrics [11]; (4) and fine-grained source code changes [10]. Dependency Graph.…”

Section: Data Collectionmentioning

confidence: 99%

“…For that, we leverage the (semantic) change information of fine-grained source code changes (SCC) [10]. To compute the prediction models we focuse on object-oriented metrics (OOM) [11] and centrality measures from social network analysis (SNA) [12] computed on the static source code dependency graph since they showed explicitly well predicting performance and in some cases achieved better performance than traditional metrics-both for change [1] and bug prediction [13].…”

Section: Introductionmentioning

confidence: 99%

Can we predict types of code changes? An empirical analysis

Giger

Pinzger

Gall

2012

2012 9th IEEE Working Conference on Mining Software Repositories (MSR)

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Abstract-There exist many approaches that help in pointing developers to the change-prone parts of a software system. Although beneficial, they mostly fall short in providing details of these changes. Fine-grained source code changes (SCC) capture such detailed code changes and their semantics on the statement level. These SCC can be condition changes, interface modifications, inserts or deletions of methods and attributes, or other kinds of statement changes. In this paper, we explore prediction models for whether a source file will be affected by a certain type of SCC. These predictions are computed on the static source code dependency graph and use social network centrality measures and object-oriented metrics. For that, we use change data of the Eclipse platform and the Azureus 3 project. The results show that Neural Network models can predict categories of SCC types. Furthermore, our models can output a list of the potentially change-prone files ranked according to their change-proneness, overall and per change type category.

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“…This gave rise to numerous experiments where researchers successfully mined such databases for interesting patterns (see [34] for an overview; specific examples can be found in [7,16,19,52]). Unfortunately, such a central database imposes a universal data schema onto all contributing tools, turning the software repository into a rigid and inflexible monolith.…”

Section: Seonmentioning

confidence: 99%

SEON: a pyramid of ontologies for software evolution and its applications

et al. 2012

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The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far.In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning.We present SEON, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time.For validation, we discuss three different approaches, which are backed by SEON and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization. Abstract The Semantic Web provides a standardized, well-established framework to define and work with ontologies. It is especially apt for machine processing. However, researchers in the field of software evolution have not really taken advantage of that so far. In this paper, we address the potential of representing software evolution knowledge with ontologies and Semantic Web technology, such as Linked Data and automated reasoning. We present Seon, a pyramid of ontologies for software evolution, which describes stakeholders, their activities, artifacts they create, and the relations among all of them. We show the use of evolution-specific ontologies for establishing a shared taxonomy of software analysis services, for defining extensible meta-models, for explicitly describing relationships among artifacts, and for linking data such as code structures, issues (change requests), bugs, and basically any changes made to a system over time. For validation, we discuss three different approaches, which are backed by Seon and enable semantically enriched software evolution analysis. These techniques have been fully implemented as tools and cover software analysis with web services, a natural language query interface for developers, and large-scale software visualization.

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Change Analysis with Evolizer and ChangeDistiller

Cited by 96 publications

References 12 publications

ProMeTA: a taxonomy for program metamodels in program reverse engineering

ProMeTA: a taxonomy for program metamodels in program reverse engineering

Can we predict types of code changes? An empirical analysis

SEON: a pyramid of ontologies for software evolution and its applications

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