research-article

Analyzing Error-Prone System Structure

Authors:

Richard W. Selby,

Victor R. BasiliAuthors Info & Claims

IEEE Transactions on Software Engineering, Volume 17, Issue 2

Pages 141 - 152

https://doi.org/10.1109/32.67595

Published: 01 February 1991 Publication History

Abstract

Using measures of data interaction called data bindings, the authors quantify ratios of coupling and strength in software systems and use the ratios to identify error-prone system structures. A 148000 source line system from a prediction environment was selected for empirical analysis. Software error data were collected from high-level system design through system testing and from field operation of the system. The authors use a set of five tools to calculate the data bindings automatically and use a clustering technique to determine a hierarchical description of each of the system's 77 subsystems. A nonparametric analysis of variance model is used to characterize subsystems and individual routines that had either many or few errors or high or low error correction effort. The empirical results support the effectiveness of the data bindings clustering approach for localizing error-prone system structure.

References

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{1} V. R. Basili, "Quantitative evaluation of software engineering methodoloy," in Proc. First Pan Pacific Computer Conf., Melbourne, Australia, Sept. 10-13, 1985; also available as Tech. Rep. TR-1519, Dep. Comput. Sci., Univ. Maryland, College Park, July 1985.

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{2} L. A. Belady and C. J. Evangelisti, "System partitioning and its measure," J. Syst. Software, vol. 2, no. 1, pp. 23-29, Feb. 1982.

Digital Library

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{3} V. R. Basili and E. E. Katz, "Metrics of interest in an ada development." in Proc. IEEE Workshop Software Engineering Technology Transfer, Miami, FL, Apr. 1983, pp. 22-29.

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{4} L. A. Belady and M. M. Lehman, "A model of large program development," IBM Syst. J., vol. 3, pp. 225-252, 1976.

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{6} V. R. Basili and B. T. Perricone, "Software errors and complexity: An empirical investigation," Commun. ACM, vol. 27, no. 1, p. 42-52, Jan. 1984.

Digital Library

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{7} V. R. Basili and R. W. Selby, "Data collection and analysis in software research and management," in Proc. Amer. Statist. Assoc. and Biometric Soc. Joint Statistical Meetings, Philadelphia, PA, Aug. 13-16, 1984.

[8]

{8} V. R. Basili, R. W. Selby, and T. Y. Phillips, "Metric analysis and data validation across Fortran projects," IEEE Trans. Software Eng., vol. SE-9, no. 6, pp. 652-663, Nov. 1983.

Digital Library

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{9} V. R. Basili and A. J. Turner, "Iterative enhancement: A practical technique for software development," IEEE Trans. Software Eng., vol. SE-1, no. 4, Dec. 1975.

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{10} V. R. Basili and D. M. Weiss, "A methodology for collecting valid software engineering data," IEEE Trans. Software Eng., vol. SE-10, no. 6, pp. 728-738, Nov. 1984.

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{14} D. H. Hutchens and V. R. Basili, "System structure analysis: Clustering with data bindings," IEEE Trans. Software Eng., vol. SE-11, no. 8, Aug. 1985.

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{17} N. Jardine and R. Sibson, Mathematical Taxonomy. New York: Wiley, 1971.

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{19} H. Scheffe, The Analysis of Variance. New York: Wiley, 1959.

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{20} R. W. Selby, "Evaluations of software technologies: Testing, cleanroom, and metrics," Ph.D. dissertation, Dep. Comput. Sci., Univ. Maryland, College Park, Tech. Rep. TR-1500, 1985.

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{21} W. P. Stevens, G. J. Myers, and L. L. Constantine, "Structural design," IBM Syst. J., vol. 13, no. 2, pp. 115-139, 1974.

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{22} V. Y. Shen, T. J. Yu, S. M. Thebaut, and L. R. Paulsen, "Identifying error-prone software--An empirical study," IEEE Trans. Software Eng., vol. SE-11, no. 4, pp. 317-324, Apr. 1985.

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Cited By

Cui DLi XLiu FWang SDai JWang LLi Q(2022)Towards Demystifying the Impact of Dependency Structures on Bug Locations in Deep Learning LibrariesProceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3544902.3546246(249-260)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3544902.3546246
Gao XFeng YHong ZMi STan J(2022)Adaptive decoupling planning method for the product crowdsourcing design tasks based on knowledge reuseExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117525206:COnline publication date: 15-Nov-2022
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Schweim DHemberg ESobania DO’Reilly U(2022)Exploiting Knowledge from Code to Guide Program SearchGenetic Programming10.1007/978-3-031-02056-8_17(262-277)Online publication date: 20-Apr-2022
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Index Terms

Analyzing Error-Prone System Structure
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Error handling and recovery
  2. Software notations and tools
    1. Software libraries and repositories

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Information & Contributors

Information

Published In

cover image IEEE Transactions on Software Engineering

IEEE Transactions on Software Engineering Volume 17, Issue 2

February 1991

111 pages

ISSN:0098-5589

Editor:
Victor R. Basili
Univ. of Maryland, College Park

Issue’s Table of Contents

Copyright © Copyright © 1991 IEEE. All Rights Reserved.

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IEEE Press

Publication History

Published: 01 February 1991

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Cui DLi XLiu FWang SDai JWang LLi Q(2022)Towards Demystifying the Impact of Dependency Structures on Bug Locations in Deep Learning LibrariesProceedings of the 16th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3544902.3546246(249-260)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1145/3544902.3546246
Gao XFeng YHong ZMi STan J(2022)Adaptive decoupling planning method for the product crowdsourcing design tasks based on knowledge reuseExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117525206:COnline publication date: 15-Nov-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117525
Schweim DHemberg ESobania DO’Reilly U(2022)Exploiting Knowledge from Code to Guide Program SearchGenetic Programming10.1007/978-3-031-02056-8_17(262-277)Online publication date: 20-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-031-02056-8_17
Cui DLiu TCai YZheng QFeng QJin WGuo JQu YAtlee JBultan TWhittle J(2019)Investigating the impact of multiple dependency structures on software defectsProceedings of the 41st International Conference on Software Engineering10.1109/ICSE.2019.00069(584-595)Online publication date: 25-May-2019
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Bian YParande MKoru GZhao S(2016)Testing the theory of relative dependency from an evolutionary perspectiveJournal of Software: Evolution and Process10.1002/smr.177428:5(340-371)Online publication date: 1-May-2016
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Kazman RCai YMo RFeng QXiao LHaziyev SFedak VShapochka ABertolino ACanfora GElbaum S(2015)A case study in locating the architectural roots of technical debtProceedings of the 37th International Conference on Software Engineering - Volume 210.5555/2819009.2819037(179-188)Online publication date: 16-May-2015
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Xiao LCai YKazman RJalote PBriand LHoek A(2014)Design rule spaces: a new form of architecture insightProceedings of the 36th International Conference on Software Engineering10.1145/2568225.2568241(967-977)Online publication date: 31-May-2014
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Oliva GSantana FGerosa Mde Souza CCleve ARobbes R(2011)Towards a classification of logical dependencies originsProceedings of the 12th International Workshop on Principles of Software Evolution and the 7th annual ERCIM Workshop on Software Evolution10.1145/2024445.2024452(31-40)Online publication date: 5-Sep-2011
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Dubey SRana A(2011)Assessment of maintainability metrics for object-oriented software systemACM SIGSOFT Software Engineering Notes10.1145/2020976.202098336:5(1-7)Online publication date: 30-Sep-2011
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