113 times Tomcat: A dataset

Destefanis, Giuseppe; Arzoky, Mahir; Counsell, Steve; Swift, Stephen; Tonelli, Roberto; Marchesi, Michele

doi:10.7287/peerj.preprints.26491

Cited by 1 publication

(2 citation statements)

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“…To analyze such properties, we considered an observation time of one release. Since in the Tomcat dataset [8], there are 113 releases, we obtained 113 points for the six time series, and measured the percentage of production classes matching the six anti-patterns. The six time series are presented in Fig.…”

Section: Methodology and Resultsmentioning

confidence: 99%

“…In this work, we present a preliminary study of the evolution (in terms of quantity) of six micro patterns of the catalog introduced by Gil and Maman [13] for 113 versions of Tomcat 1 , an open source Java Servlet Container developed by the Apache Software Foundation (from version 3.3.2 to version 8.0.9) and heavily used in software engineering research [20]. We enriched the dataset provided by Destefanis et al [8], detecting the micro patterns from the source code of each version of Tomcat. The new dataset is openly available at the following link https:// bitbucket.org/ giuseppedestefanis/ promise2018 and contains 113 new tables with all the micro patterns for each version of Tomcat.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Longitudinal Study of Anti Micro Patterns in 113 versions of Tomcat

Destefanis

Qaderi

Bowes

et al. 2018

Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering

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Background: Micro patterns represent design decisions in code. They are similar to design patterns and can be detected automatically. These micro structures can be helpful in identifying portions of code which should be improved (anti-micro patterns), or other well-designed parts which need to be preserved. The concepts expressed in these design decisions are dened at class-level; therefore the primary goal is to detect and provide information related to a specic granularity level. Aim: this paper aims to present preliminary results about a longitudinal study performed on anti-micro pattern distributions over 113 versions of Tomcat. Method: we rst extracted the micro patterns from the 113 versions of Tomcat, then found the percentage of classes matching each of the six anti-micro pattern considered for this analysis, and studied correlations among the obtained time series after testing for stationarity, randomness and seasonality. Results: results show that the time series are stationary, not random (except for Function Pointer), and that additional studied are needed for studying seasonality. Regarding correlations, only the Pool and Record time series presented a correlation of 0.69, while moderate correlation has been found between Function Pointer and Function Object (0.58) and between Cobol Like and Pool (0.44).

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Section: Methodology and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%