From Reality to Programs and (Not Quite) Back Again

Ratiu, Daniel; Deissenboeck, Florian

doi:10.1109/icpc.2007.22

Cited by 36 publications

(33 citation statements)

References 12 publications

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“…The Program Ontology used is in line with other authors' proposed descriptions (e.g. [37,45,46]). This also eases future integration processes with other tools that followed those same approaches.…”

Section: Normalizing Information Populating Ontologiessupporting

confidence: 49%

Conclave: Ontology-Driven Measurement of Semantic Relatedness between Source Code Elements and Problem Domain Concepts

Carvalho

Almeida

Henriques

et al. 2014

Computational Science and Its Applications – ICCSA 2014

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Abstract. Software maintainers are often challenged with source code changes to improve software systems, or eliminate defects, in unfamiliar programs. To undertake these tasks a sufficient understanding of the system (or at least a small part of it) is required. One of the most time consuming tasks of this process is locating which parts of the code are responsible for some key functionality or feature. Feature (or concept) location techniques address this problem. This paper introduces Conclave, an environment for software analysis, and in particular the Conclave-Mapper tool that provides a feature location facility. This tool explores natural language terms used in programs (e.g. function and variable names), and using textual analysis and a collection of Natural Language Processing techniques, computes synonymous sets of terms. These sets are used to score relatedness between program elements, and search queries or problem domain concepts, producing sorted ranks of program elements that address the search criteria, or concepts. An empirical study is also discussed to evaluate the underlying feature location technique.

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Section: Normalizing Information Populating Ontologiessupporting

confidence: 49%

Conclave: Ontology-Driven Measurement of Semantic Relatedness between Source Code Elements and Problem Domain Concepts

Carvalho

Almeida

Henriques

et al. 2014

Computational Science and Its Applications – ICCSA 2014

View full text Add to dashboard Cite

show abstract

“…Admittedly, the cost of mapping features to code can be expensive, but research areas such as feature location are focused on automatically recovering such mappings. For instance, Ratiu et al (Ratiu and Deissenboeck 2006;Ratiu and Deissenboeck 2007) have developed a formal framework for mapping domain concepts to program elements. Also, some integrated development environments like IBM's Jazz 5 have embedded automatic traceability functionalities for requirements and bug fixes that could be leveraged.…”

Section: Measurement Toolmentioning

confidence: 99%

Using structural and textual information to capture feature coupling in object-oriented software

2011

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Previous studies have demonstrated the relationship between coupling and external software quality attributes, such as fault-proneness, and the application of coupling to software maintenance tasks, such as impact analysis. These previous studies concentrate on class coupling. However, there is a growing focus on the study of features in software, and features are often implemented across multiple classes, meaning class-level coupling measures are not applicable. We ask the pertinent question, "Is measuring coupling at the feature-level also useful?" We define new feature coupling metrics based on structural and textual source code information and extend the unified framework for coupling measurement to include these new metrics. We also conduct three extensive case studies to evaluate these new metrics and answer this research question. The first study examines the relationship between feature coupling and fault-proneness, the second assesses feature coupling in the context of impact analysis, and the third study surveys developers to determine if the metrics align with what they consider to be coupled features. All three studies provide evidence that feature coupling metrics are indeed useful new measures that capture coupling at a higher level of abstraction than classes and can be useful for finding bugs, guiding testing effort, and assessing impact of changes.

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“…One key advantage to using API packages, classes, and methods as attributes is that these attributes are more stable than terms across many programs. Recent work has found that APIs are more likely to represent domain concepts in applications than terms (Ratiu and Deissenboeck 2006;Ratiu and Deissenboeck 2007). Hence, APIs are likely to be high-quality attributes for categorization, even if terms are not.…”

Section: Discussionmentioning

confidence: 99%

On using machine learning to automatically classify software applications into domain categories

et al. 2012

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Software repositories hold applications that are often categorized to improve the effectiveness of various maintenance tasks. Properly categorized applications allow stakeholders to identify requirements related to their applications and predict maintenance problems in software projects. Manual categorization is expensive, tedious, and laborious -this is why automatic categorization approaches are gaining widespread importance. Unfortunately, for different legal and organizational reasons, the applications' source code is often not available, thus making it difficult to automatically categorize these applications. In this paper, we propose a novel approach in which we use Application Programming Interface (API) calls from third-party libraries for automatic categorization of software applications that use these API calls. Our approach is general since it enables different categorization algorithms to be applied to repositories that contain both source code and bytecode of applications, since API calls can be extracted from both the source code and byte-code. We compare our approach to a state-of-the-art approach that uses machine learning algorithms for software categorization, and conduct experiments on two large Java repositories: an open-source repository containing 3,286 projects and a closed-source repository with 745 applications, where the source code was not available. Our contribution is twofold: we propose a new approach that makes it possible to categorize software projects without any source code using a small number of API calls as attributes, and furthermore we carried out a comprehensive empirical evaluation of automatic categorization approaches.

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From Reality to Programs and (Not Quite) Back Again

Cited by 36 publications

References 12 publications

Conclave: Ontology-Driven Measurement of Semantic Relatedness between Source Code Elements and Problem Domain Concepts

Conclave: Ontology-Driven Measurement of Semantic Relatedness between Source Code Elements and Problem Domain Concepts

Using structural and textual information to capture feature coupling in object-oriented software

On using machine learning to automatically classify software applications into domain categories

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