Machine learning to evaluate evolvability defects: Code metrics thresholds for a given context

Naohiko Tsuda; Hironori Washizaki; Yoshiaki Fukazawa; Yuichiro Yasuda; Shunsuke Sugimura

doi:10.1109/QRS.2018.00022

Machine learning to evaluate evolvability defects: Code metrics thresholds for a given context

Naohiko Tsuda, Hironori Washizaki, Yoshiaki Fukazawa, Yuichiro Yasuda, Shunsuke Sugimura

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Evolvability defects are non-understandable and non-modifiable states that do not directly produce runtime behavioral failures. Automatic source code evaluation by metrics and thresholds can help reduce the burden of a manual inspection. This study addresses two problems. (1) Evolvability defects are not usually managed in bug tracking systems. (2) Conventional methods cannot fully interpret the relations among the metrics in a given context (e.g., programming language, application domain). The key actions of our method are to (1) gather trainingdata for machine learning by experts' manual inspection of some of the files in given systems (benchmark) and (2) employ a classification-tree learner algorithm, C5.0, which can deal with non-orthogonal relations between metrics. Furthermore, we experimentally confirm that, even with less training-data, our method provides a more precise evaluation than four conventional methods (the percentile, Alves' method, Bender's method, and the ROC curve-based method).

Original language	English
Title of host publication	Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	83-94
Number of pages	12
ISBN (Print)	9781538677575
DOIs	https://doi.org/10.1109/QRS.2018.00022
Publication status	Published - 2018 Aug 2
Event	18th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2018 - Lisbon, Portugal Duration: 2018 Jul 16 → 2018 Jul 20

Other

Other	18th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2018
Country	Portugal
City	Lisbon
Period	18/7/16 → 18/7/20

Keywords

Classification-tree
Contextual thresholds
Evolvability defects
Goal-Question-Metrics (GQM)
Machine learning
Software metrics

ASJC Scopus subject areas

Software
Safety, Risk, Reliability and Quality

Access to Document

10.1109/QRS.2018.00022

Fingerprint Dive into the research topics of 'Machine learning to evaluate evolvability defects: Code metrics thresholds for a given context'. Together they form a unique fingerprint.

Cite this

Tsuda, N., Washizaki, H., Fukazawa, Y., Yasuda, Y., & Sugimura, S. (2018). Machine learning to evaluate evolvability defects: Code metrics thresholds for a given context. In Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018 (pp. 83-94). [8424960] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/QRS.2018.00022

Machine learning to evaluate evolvability defects : Code metrics thresholds for a given context. / Tsuda, Naohiko ; Washizaki, Hironori ; Fukazawa, Yoshiaki; Yasuda, Yuichiro; Sugimura, Shunsuke.

Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 83-94 8424960.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tsuda, N , Washizaki, H , Fukazawa, Y, Yasuda, Y & Sugimura, S 2018, Machine learning to evaluate evolvability defects: Code metrics thresholds for a given context. in Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018., 8424960, Institute of Electrical and Electronics Engineers Inc., pp. 83-94, 18th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2018, Lisbon, Portugal, 18/7/16. https://doi.org/10.1109/QRS.2018.00022

Tsuda N , Washizaki H , Fukazawa Y, Yasuda Y, Sugimura S. Machine learning to evaluate evolvability defects: Code metrics thresholds for a given context. In Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 83-94. 8424960 https://doi.org/10.1109/QRS.2018.00022

Tsuda, Naohiko ; Washizaki, Hironori ; Fukazawa, Yoshiaki ; Yasuda, Yuichiro ; Sugimura, Shunsuke. / Machine learning to evaluate evolvability defects : Code metrics thresholds for a given context. Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 83-94

@inproceedings{e78c40c7238c4045819bb850fa0ea9f3,

title = "Machine learning to evaluate evolvability defects: Code metrics thresholds for a given context",

abstract = "Evolvability defects are non-understandable and non-modifiable states that do not directly produce runtime behavioral failures. Automatic source code evaluation by metrics and thresholds can help reduce the burden of a manual inspection. This study addresses two problems. (1) Evolvability defects are not usually managed in bug tracking systems. (2) Conventional methods cannot fully interpret the relations among the metrics in a given context (e.g., programming language, application domain). The key actions of our method are to (1) gather trainingdata for machine learning by experts' manual inspection of some of the files in given systems (benchmark) and (2) employ a classification-tree learner algorithm, C5.0, which can deal with non-orthogonal relations between metrics. Furthermore, we experimentally confirm that, even with less training-data, our method provides a more precise evaluation than four conventional methods (the percentile, Alves' method, Bender's method, and the ROC curve-based method).",

keywords = "Classification-tree, Contextual thresholds, Evolvability defects, Goal-Question-Metrics (GQM), Machine learning, Software metrics",

author = "Naohiko Tsuda and Hironori Washizaki and Yoshiaki Fukazawa and Yuichiro Yasuda and Shunsuke Sugimura",

year = "2018",

month = aug,

day = "2",

doi = "10.1109/QRS.2018.00022",

language = "English",

isbn = "9781538677575",

pages = "83--94",

booktitle = "Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "18th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2018 ; Conference date: 16-07-2018 Through 20-07-2018",

}

TY - GEN

T1 - Machine learning to evaluate evolvability defects

T2 - 18th IEEE International Conference on Software Quality, Reliability, and Security, QRS 2018

AU - Tsuda, Naohiko

AU - Washizaki, Hironori

AU - Fukazawa, Yoshiaki

AU - Yasuda, Yuichiro

AU - Sugimura, Shunsuke

PY - 2018/8/2

Y1 - 2018/8/2

N2 - Evolvability defects are non-understandable and non-modifiable states that do not directly produce runtime behavioral failures. Automatic source code evaluation by metrics and thresholds can help reduce the burden of a manual inspection. This study addresses two problems. (1) Evolvability defects are not usually managed in bug tracking systems. (2) Conventional methods cannot fully interpret the relations among the metrics in a given context (e.g., programming language, application domain). The key actions of our method are to (1) gather trainingdata for machine learning by experts' manual inspection of some of the files in given systems (benchmark) and (2) employ a classification-tree learner algorithm, C5.0, which can deal with non-orthogonal relations between metrics. Furthermore, we experimentally confirm that, even with less training-data, our method provides a more precise evaluation than four conventional methods (the percentile, Alves' method, Bender's method, and the ROC curve-based method).

AB - Evolvability defects are non-understandable and non-modifiable states that do not directly produce runtime behavioral failures. Automatic source code evaluation by metrics and thresholds can help reduce the burden of a manual inspection. This study addresses two problems. (1) Evolvability defects are not usually managed in bug tracking systems. (2) Conventional methods cannot fully interpret the relations among the metrics in a given context (e.g., programming language, application domain). The key actions of our method are to (1) gather trainingdata for machine learning by experts' manual inspection of some of the files in given systems (benchmark) and (2) employ a classification-tree learner algorithm, C5.0, which can deal with non-orthogonal relations between metrics. Furthermore, we experimentally confirm that, even with less training-data, our method provides a more precise evaluation than four conventional methods (the percentile, Alves' method, Bender's method, and the ROC curve-based method).

KW - Classification-tree

KW - Contextual thresholds

KW - Evolvability defects

KW - Goal-Question-Metrics (GQM)

KW - Machine learning

KW - Software metrics

UR - http://www.scopus.com/inward/record.url?scp=85052334218&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052334218&partnerID=8YFLogxK

U2 - 10.1109/QRS.2018.00022

DO - 10.1109/QRS.2018.00022

M3 - Conference contribution

AN - SCOPUS:85052334218

SN - 9781538677575

SP - 83

EP - 94

BT - Proceedings - 2018 IEEE 18th International Conference on Software Quality, Reliability, and Security, QRS 2018

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 16 July 2018 through 20 July 2018

ER -