A semantic entropy metric

Etzkorn, Letha H.; Gholston, Sampson; Hughes, William E.

doi:10.1002/smr.255

Cited by 22 publications

(19 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Etzkorn et al [15] presented a new measure for object-oriented programs that examines the implementation domain content of a class to measure its complexity. The content of methods in a class has also been exploited to measure the conceptual cohesion of a class [2], [3], [16].…”

Section: A Entropy and Ir-based Metricsmentioning

confidence: 99%

Physical and conceptual identifier dispersion: Measures and relation to fault proneness

Arnaoudova

Eshkevari

Oliveto

et al. 2010

2010 IEEE International Conference on Software Maintenance

View full text Add to dashboard Cite

Abstract-Poorly-chosen identifiers have been reported in the literature as misleading and increasing the program comprehension effort. Identifiers are composed of terms, which can be dictionary words, acronyms, contractions, or simple strings. We conjecture that the use of identical terms in different contexts may increase the risk of faults. We investigate our conjecture using a measure combining term entropy and term context coverage to study whether certain terms increase the odds ratios of methods to be fault-prone. Entropy measures the physical dispersion of terms in a program: the higher the entropy, the more scattered across the program the terms. Context coverage measures the conceptual dispersion of terms: the higher their context coverage, the more unrelated the methods using them. We compute term entropy and context coverage of terms extracted from identifiers in Rhino 1.4R3 and ArgoUML 0.16. We show statistically that methods containing terms with high entropy and context coverage are more fault-prone than others.

show abstract

Section: A Entropy and Ir-based Metricsmentioning

confidence: 99%

Physical and conceptual identifier dispersion: Measures and relation to fault proneness

Arnaoudova

Eshkevari

Oliveto

et al. 2010

2010 IEEE International Conference on Software Maintenance

View full text Add to dashboard Cite

show abstract

“…The RCDE metrics are based on the Semantic Class Definition Entropy (SCDE) metric defined by Etzkorn, Gholston, and Hughes [3]. SCDE is a complexity metric that uses information theoretic and program understanding approaches to assess the domain information content of a class.…”

Section: Rcde Metricsmentioning

confidence: 99%

“…First, they can be biased by insignificant differences in coding style. For example, it has been shown [3] that small coding choices, such as placing multiple functions in a single line of code rather than multiple lines, can yield different syntactical metrics results, although there is no difference in the complexity of the function performed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Empirical Validation of the RCDC and RCDE Semantic Complexity Metrics for Object-Oriented Software

Cox¹,

Gholston²,

Utley³

et al. 2007

CIT

Self Cite

View full text Add to dashboard Cite

The Relative Class Domain Complexity (RCDC) and Relative Class Definition Entropy (RCDE) semantic metrics have been proposed for use as complexity metrics for object-oriented software. These semantic metrics are calculated on a knowledge-based representation of software, following a knowledge-based program understanding examination of the software. The metrics have great potential because they can be applied during the software design phase whereas most complexity metrics cannot be applied until after development is complete. In this paper, we present the results of a study to empirically validate the RCDC and RCDE metrics. We show that the metrics compare favorably with the findings of human experts and also that they correlate well with the results of conventional complexity metrics.

show abstract

“…Further it contains short descriptive taxonomy of the object-oriented Design and metrics. Semantic metrics [7] describes about the semantically-based metric for object-oriented systems, called the Semantic Class Definition Entropy (SCDE) metric, which examines the implementation domain content of a class to measure class complexity. The domain content is determined using a knowledge-based program understanding system.…”

Section: Introductionmentioning

confidence: 99%

Conceptual and Semantic Measures for Cohesion in Software Maintenance

Mishra¹,

Srivastava²

2012

IJCA

View full text Add to dashboard Cite

In software maintenance, cohesion plays very major role to determine the relationship among different software attributes such as class, method, function-type etc. There are many method have been used in this context such as method based on syntactically keyword count in source code. We have used the semantic value computation for the specific keyword occurs in distinct common method within the different classes for an open source code project. We have also computed the conceptual relation metric to analysis the cohesion for the method within their class. Also, there is comparison between different semantic values for the keyword of common method in this context.

show abstract

A semantic entropy metric

Cited by 22 publications

References 15 publications

Physical and conceptual identifier dispersion: Measures and relation to fault proneness

Physical and conceptual identifier dispersion: Measures and relation to fault proneness

Empirical Validation of the RCDC and RCDE Semantic Complexity Metrics for Object-Oriented Software

Conceptual and Semantic Measures for Cohesion in Software Maintenance

Contact Info

Product

Resources

About