Software engineering: reliability, development, and management.
Software engineering: reliability, development, and management.
Comments on :20Estimating the number of faults in code" and two corrections to published data
IEEE Transactions on Software Engineering
Robust regression and outlier detection
Robust regression and outlier detection
Validating Halstead's Theory for Pascal Programs
IEEE Transactions on Software Engineering
Prediction and control of ADA software defects
Journal of Systems and Software - An Oregon workshop on software metrics
Stochastic models for software science
Journal of Systems and Software
Software engineering, the software process and their support
Software Engineering Journal - Special issue on software process and its support
On the prediction of computer implementation faults via static error prediction models
Journal of Systems and Software
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
On estimating the number of defects remaining in software
Journal of Systems and Software
Software defect and operational profile modeling
Software defect and operational profile modeling
A Review and Evaluation of Software Science
ACM Computing Surveys (CSUR)
Software Complexity: Measures and Methods
Software Complexity: Measures and Methods
Elements of Software Science (Operating and programming systems series)
Elements of Software Science (Operating and programming systems series)
Reexamining the Fault Density-Component Size Connection
IEEE Software
Laws of Software Evolution Revisited
EWSPT '96 Proceedings of the 5th European Workshop on Software Process Technology
Predicting numbers of errors using software science
Proceedings of the 1981 ACM workshop/symposium on Measurement and evaluation of software quality
Some experimental estimators for developmental and delivered errors in software development projects
Proceedings of the 1981 ACM workshop/symposium on Measurement and evaluation of software quality
Information Sciences: an International Journal
Does software reliability growth behavior follow a non-homogeneous Poisson process
Information and Software Technology
Software execution processes as an evolving complex network
Information Sciences: an International Journal
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Halstead's software science postulates that there exist physics-like laws that obey each piece of software. In this paper we reexamine this postulate by using two datasets collected from real programs, and argue that software science data are featured with partial repeatability. Conventional sciences embody the nature of full repeatability in the sense that they can either be proved repeatably in mathematics or be validated to a high accuracy repeatably in physics (experimentally). By partial repeatability we mean that complex phenomena may demonstrate an invariant property that neither can be proved in mathematics nor validated to a high accuracy in physics, but still (partially) governs the behavior of the phenomena. We propose a new kind of mathematical model, namely, parepeatic model, to characterize partial repeatability quantitatively. A parepeatic model defines the relationship between a central function and a fluctuation zone and identifies the degree of correctness of the relationship without making any statistical assumption. We develop parepeatic models for the relationships among several program complexity measures including the number of distinct operators, the number of distinct operands and the program length, among others, and present some new findings about the relationships. Illustrative case study shows that the developed parepeatic models can really help software engineering practice.