Physical and Conceptual Identifier Dispersion: Measures and Relation to Fault Proneness. Arnaoudova, V., Eshkevari, L. M., Oliveto, R., Gu�h�neuc, Y., & Antoniol, G. In Ferenc, R. & Poshyvanyk, D., editors, Proceedings of the 26<sup>th</sup> International Conference on Software Maintenance (ICSM), pages 1–5, September, 2010. IEEE CS Press. 5 pages. Early Research Achievements Track. Best paper.
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Paper abstract bibtex
Paper abstract bibtex 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.
@INPROCEEDINGS{Arnaoudova10-ICSM-ERA-IdentifierEntropy,
AUTHOR = {Venera Arnaoudova and Laleh Mousavi Eshkevari and
Rocco Oliveto and Yann-Ga�l Gu�h�neuc and Giuliano Antoniol},
BOOKTITLE = {Proceedings of the 26<sup>th</sup> International Conference on Software Maintenance (ICSM)},
TITLE = {Physical and Conceptual Identifier Dispersion: Measures
and Relation to Fault Proneness},
YEAR = {2010},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Rudolf Ferenc and Denys Poshyvanyk},
MONTH = {September},
NOTE = {5 pages. Early Research Achievements Track. Best paper.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {1–5},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Identifier analyses</b>,
Rubrique : <b>analyses des identifiants</b>, Conference: ICSM,
Conference: ICSME},
URL = {http://www.ptidej.net/publications/documents/ICSM10a.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/ICSM10a.ppt.pdf},
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.}
}Downloads: 0
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