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Measuring the Psychological Complexity of Software Maintenance Tasks with the Halstead and McCabe Metrics

Published: 01 March 1979 Publication History

Abstract

Three software complexity measures (Halstead's E, McCabe's u(G), and the length as measured by number of statements) were compared to programmer performance on two software maintenance tasks. In an experiment on understanding, length and u(G) correlated with the percent of statements correctly recalled. In an experiment on modification, most significant correlations were obtained with metrics computed on modified rather than unmodified code. All three metrics correlated with both the accuracy of the modification and the time to completion. Relationships in both experiments occurred primarily in unstructured rather than structured code, and in code with no comments. The metrics were also most predictive of performance for less experienced programmers. Thus, these metrics appear to assess psychological complexity primarily where programming practices do not provide assistance in understanding the code.

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Published In

cover image IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering  Volume 5, Issue 2
March 1979
105 pages

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IEEE Press

Publication History

Published: 01 March 1979

Author Tags

  1. Commenting
  2. Halstead's E
  3. McCabe's u(G)
  4. complexity metrics
  5. documentation
  6. human factors in software engineering
  7. mnemonic variable names
  8. modem programming practices modifica-tions
  9. software science
  10. structured programming

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Cited By

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  • (2024)Tools and Models for Software Reverse Engineering ResearchProceedings of the 2024 Workshop on Research on offensive and defensive techniques in the context of Man At The End (MATE) attacks10.1145/3689934.3690817(44-58)Online publication date: 19-Nov-2024
  • (2024)Method-level Bug Prediction: Problems and PromisesACM Transactions on Software Engineering and Methodology10.1145/364033133:4(1-31)Online publication date: 13-Jan-2024
  • (2023)BFSig: Leveraging File Significance in Bus Factor EstimationProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3613877(1926-1936)Online publication date: 30-Nov-2023
  • (2022)An empirical study on maintainable method size in JavaProceedings of the 19th International Conference on Mining Software Repositories10.1145/3524842.3527975(252-264)Online publication date: 23-May-2022
  • (2022)Revisiting the debate: Are code metrics useful for measuring maintenance effort?Empirical Software Engineering10.1007/s10664-022-10193-827:6Online publication date: 1-Nov-2022
  • (2022)Semi-supervised multitask learning using convolutional autoencoder for faulty code detection with limited dataApplied Intelligence10.1007/s10489-022-03663-553:4(3877-3888)Online publication date: 4-Jun-2022
  • (2021)Program Comprehension and Code Complexity MetricsProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00056(524-536)Online publication date: 22-May-2021
  • (2019)A class project to prepare software engineering students for their capstone projectsProceedings of the 8th Computer Science Education Research Conference10.1145/3375258.3375267(66-78)Online publication date: 18-Nov-2019
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  • (2019)A Comparison Study of VAE and GAN for Software Fault PredictionAlgorithms and Architectures for Parallel Processing10.1007/978-3-030-38961-1_8(82-96)Online publication date: 9-Dec-2019
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