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Metrics As Scores Dataset: Metrics and Domains From the Qualitas.class corpus

Authors: Sebastian Hönel;

Metrics As Scores Dataset: Metrics and Domains From the Qualitas.class corpus

Abstract

This dataset was created by extracting software metrics data from the Qualitas.class corpus (Terra et al. 2013; Tempero et al. 2010). Therefore, the principal quantity type is Metric, and the context is given by a system’s Domain (e.g., "Game", "Middleware", etc.). Some metrics were obtained on program-level, while others are package- or method-level metrics. Most of the metrics in the corpus are of discrete/integral nature. The corpus holds 23 types of pre-computed software metrics for a total of 111 systems which are spread across eleven different domains. This dataset has the following discrete Features (Metrics): CA: Afferent Coupling CE: Efferent Coupling DIT: Depth of Inheritance Tree MLOC: Method Lines of Code NBD: Nested Block Depth NOC: Number of Classes NOF: Number of Attributes NOI: Number of Interfaces NOM: Number of Methods NOP: Number of Packages NORM: Number of Overridden Methods NSC: Number of Children NSF: Number of Static Attributes NSM: Number of Static Methods PAR: Number of Parameters TLOC: Total Lines of Code VG: McCabe Cyclomatic Complexity WMC: Weighted Methods per Class The following features are continuous: LCOM: Lack of Cohesion in Methods RMA: Abstractness RMD: Normalized Distance RMI: Instability SIX: Specialization Index It has a total of 11 Groups (Domains): 3D; Graphics; Media, Databases, Diagrams; Visualiz., Games, IDE, Middleware, Parsers; Generators, Progr. Language, SDK, Testing, and Tool.

{"references": ["H\u00f6nel, Sebastian, Morgan Ericsson, Welf L\u00f6we, and Anna Wingkvist. 2022. \"Contextual Operationalization of Metrics as Scores: Is My Metric Value Good?\" In 22nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2022, Guangzhou, China, December 5-9, 2022, 333\u201343. IEEE. https://doi.org/10.1109/QRS57517.2022.00042.", "Terra, Ricardo, Luis Fernando Miranda, Marco T\u00falio Valente, and Roberto da Silva Bigonha. 2013. \"Qualitas.class Corpus: A Compiled Version of the Qualitas Corpus.\" ACM SIGSOFT Softw. Eng. Notes 38 (5): 1\u20134. https://doi.org/10.1145/2507288.2507314.", "Tempero, Ewan D., Craig Anslow, Jens Dietrich, Ted Han, Jing Li, Markus Lumpe, Hayden Melton, and James Noble. 2010. \"The Qualitas Corpus: A Curated Collection of Java Code for Empirical Studies.\" In 17th Asia Pacific Software Engineering Conference, APSEC 2010, Sydney, Australia, November 30 - December 3, 2010, edited by Jun Han and Tran Dan Thu, 336\u201345. IEEE Computer Society. https://doi.org/10.1109/APSEC.2010.46."]}

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Keywords

Software Systems and Domains, Qualitas.class corpus, Software Metrics, Metrics As Scores

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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