research-article

A Bayesian network approach to assist on the interpretation of software metrics

Authors:

Mirko Perkusich,

Amaury Medeiros,

Lenardo Chaves e Silva,

Kyller Costa Gorgônio,

Hyggo Oliveira de Almeida,

Angelo PerkusichAuthors Info & Claims

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

Pages 1498 - 1503

https://doi.org/10.1145/2695664.2695749

Published: 13 April 2015 Publication History

Abstract

Despite the quantity of software metrics that has been proposed, their adoption and application by practitioners has been limited. A challenge to their use is to interpret them to perform assessments and predictions. The existing approaches to assist with their interpretation consists of defining thresholds to determine whether the value of a metric is acceptable. These approaches are not enough to ensure a correct metrics' interpretation, because they ignore risks and other subjective factors that influence the measurement process. This might affect the metrics' interpretation, and consequently, the manager's decision. To minimize wrong decisions based on software metrics, we present a method to construct Bayesian networks to assist on metric interpretation considering these risks. We successfully validated the method with a case study performed in three software development projects. We concluded that it is a promising approach to assist practitioners to interpret metrics and support software projects managerial decision-making.

References

[1]

M. Abouelela and L. Benedicenti. Bayesian network based xp process modelling. International Journal of Software Engineering and Applications, 1(3):1--15, 2010.

[2]

M. A. Ahmed and Z. Muzaffar. Handling imprecision and uncertainty in software development effort prediction: A type-2 fuzzy logic based framework. Information and Software Technology, 51(3): 640--654, 2009.

Digital Library

[3]

N. Fenton and M. Neil. Software metrics and risk. In Proc 2nd European Software Measurement Conference (FESMA'99), TI-KVIV, Amsterdam, ISBN, pages 90--76019, 1999.

[4]

N. Fenton and M. Neil. Risk Assessment and Decision Analysis with Bayesian Networks. CRC Press, 1 edition, 11 2012.

Digital Library

[5]

N. E. Fenton and M. Neil. Software metrics: Roadmap. In Proceedings of the Conference on The Future of Software Engineering, ICSE '00, pages 357--370, New York, NY, USA, 2000. ACM.

Digital Library

[6]

N. E. Fenton, M. Neil, and J. G. Caballero. Using ranked nodes to model qualitative judgments in bayesian networks. IEEE Trans. on Knowl. and Data Eng., 19(10):1420--1432, Oct. 2007.

Digital Library

[7]

N. E. Fenton, M. Neil, and D. A. Lagnado. A general structure for legal arguments about evidence using bayesian networks. Cognitive Science, 37(1):61--102, 2013.

[8]

K. A. Ferreira, M. A. Bigonha, R. S. Bigonha, L. F. Mendes, and H. C. Almeida. Identifying thresholds for object-oriented software metrics. Journal of Systems and Software, 85(2): 244--257, 2012.

Digital Library

[9]

M. Foucault, M. Palyart, J.-R. Falleri, and X. Blanc. Computing contextual metric thresholds. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC '14, pages 1120--1125, New York, NY, USA, 2014. ACM.

Digital Library

[10]

B. Kitchenham. What's up with software metrics? a preliminary mapping study. Journal of Systems and Software, 83(1): 37--51, 2010. SI: Top Scholars.

Digital Library

[11]

H. Koziolek. Goal, question, metric. In Dependability metrics, pages 39--42. Springer, 2008.

Digital Library

[12]

D. Montini, F. Cardoso, F. Marcondes, P. Tasinaffo, L. A. V. Dias, and A. da Cunha. Using gqm hypothesis restriction to infer bayesian network testing. In Information Technology: New Generations, 2009. ITNG '09. Sixth International Conference on, pages 1436--1441, April 2009.

Digital Library

[13]

M. Perkusich, H. O. de Almeida, and A. Perkusich. A model to detect problems on scrum-based software development projects. In Proceedings of the 28th Annual ACM Symposium on Applied Computing, pages 1037--1042. ACM, 2013.

Digital Library

[14]

M. Perkusich, A. Perkusich, and H. O. d. Almeida. Using survey and weighted functions to generate node probability tables for bayesian networks. In Proceedings of the 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, BRICS-CCI-CBIC '13, pages 183--188, Ipojuca, Brazil, 2013. IEEE Computer Society.

Digital Library

[15]

M. Perkusich, G. Soares, H. Almeida, and A. Perkusich. A procedure to detect problems of processes in software development projects using bayesian networks. Expert Systems with Applications, 42(1): 437--450, 2015.

Digital Library

[16]

B. Spasic and B. Onggo. Agent-based simulation of the software development process: A case study at avl. In Simulation Conference (WSC), Proceedings of the 2012 Winter, pages 1--11, Dec 2012.

Digital Library

[17]

S. Wagner. A bayesian network approach to assess and predict software quality using activity-based quality models. Information and Software Technology, 52(11): 1230--1241, 2010.

Digital Library

[18]

L. G. Wallace and S. D. Sheetz. The adoption of software measures: A technology acceptance model (tam) perspective. Information & Management, 51(2): 249--259, 2014.

Digital Library

Cited By

de Sousa Ade Souza CReis R(2021)A 20‐year mapping of Bayesian belief networks in software project managementIET Software10.1049/sfw2.1204316:1(14-28)Online publication date: 9-Nov-2021
https://doi.org/10.1049/sfw2.12043
Saraiva RMedeiros APerkusich MValadares DGorgonio KPerkusich AAlmeida H(2020)A Bayesian Networks-Based Method to Analyze the Validity of the Data of Software Measurement ProgramsIEEE Access10.1109/ACCESS.2020.30352178(198801-198821)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3035217
Mori AVale GCirilo EFigueiredo E(2019)A Domain-Sensitive Threshold Derivation MethodProceedings of the XV Brazilian Symposium on Information Systems10.1145/3330204.3330252(1-8)Online publication date: 20-May-2019
https://dl.acm.org/doi/10.1145/3330204.3330252
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cover image ACM Conferences

SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

April 2015

2418 pages

ISBN:9781450331968

DOI:10.1145/2695664

Conference Chairs:
Roger L. Wainwright
University of Tulsa
,
Juan Manuel Corchado
University of Salamanca, Spain
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Jiman Hong
Soongsil University, South Korea

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 13 April 2015

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SAC 2015: Symposium on Applied Computing

April 13 - 17, 2015

Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

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Cited By

de Sousa Ade Souza CReis R(2021)A 20‐year mapping of Bayesian belief networks in software project managementIET Software10.1049/sfw2.1204316:1(14-28)Online publication date: 9-Nov-2021
https://doi.org/10.1049/sfw2.12043
Saraiva RMedeiros APerkusich MValadares DGorgonio KPerkusich AAlmeida H(2020)A Bayesian Networks-Based Method to Analyze the Validity of the Data of Software Measurement ProgramsIEEE Access10.1109/ACCESS.2020.30352178(198801-198821)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3035217
Mori AVale GCirilo EFigueiredo E(2019)A Domain-Sensitive Threshold Derivation MethodProceedings of the XV Brazilian Symposium on Information Systems10.1145/3330204.3330252(1-8)Online publication date: 20-May-2019
https://dl.acm.org/doi/10.1145/3330204.3330252
Vale GFernandes EFigueiredo E(2019)On the proposal and evaluation of a benchmark-based threshold derivation methodSoftware Quality Journal10.1007/s11219-018-9405-y27:1(275-306)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s11219-018-9405-y
Mori AVale GViggiato MOliveira JFigueiredo ECirilo EJamshidi PKastner CNord RBuschmann FKruchten P(2018)Evaluating domain-specific metric thresholdsProceedings of the 2018 International Conference on Technical Debt10.1145/3194164.3194173(41-50)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3194164.3194173
de Bassi PWanderley GBanali PParaiso E(2018)Measuring Developers' Contribution in Source Code using Quality Metrics2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD))10.1109/CSCWD.2018.8465320(39-44)Online publication date: May-2018
https://doi.org/10.1109/CSCWD.2018.8465320

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