An experimental investigation of software metrics and their relationship to software development effort

Lind, Regina; Vairavan, K.

doi:10.1109/32.24715

Cited by 83 publications

(29 citation statements)

References 7 publications

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“…Gremillion's main finding was that the number of program defects was significantly related to the complexity metrics, and in particular that the best single predictor metric was SLOC. Essentially the same results were found by Lind and Vairavan in a study of a number of releases of a large medical imaging system [Lind and Vairavan 1989]. They found a high correlation between the more complex metrics and SLOC, and found that SLOC was the best single predictor of number of "system performance reports" and development effort.…”

Section: Relationships Among Metrics and Maintenancesupporting

confidence: 76%

Software complexity and software maintenance: A survey of empirical research

Kemerer

1995

Ann Software Eng

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A number of empirical studies have pointed to a link between software complexity and software maintenance performance. The primary purpose of this paper is to document "what is known" about this relationship, and to suggest some possible future avenues of research. In particular, a survey of the empirical literature in this area shows two broad areas of study: complexity metrics and comprehension. Much of the complexity metrics research has focused on modularity and structure metrics. The articles surveyed are summarized as to major differences and similarities in a set of detailed tables. The text is used to highlight major findings and differences, and a concluding remarks section provides a series of recommendations for future research.

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Section: Relationships Among Metrics and Maintenancesupporting

confidence: 76%

Software complexity and software maintenance: A survey of empirical research

Kemerer

1995

Ann Software Eng

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“…MIS development took five years, and the system has been in commercial use at several hundred sites for three years. Lind and Vairavan collected Changes, the number of changes made to each module due to faults discovered during system testing and maintenance [Lind and Vairavan 1989]. They also collected eleven software complexity metrics for each of the modules that comprise MIS.…”

Section: The Medical Imaging Systemmentioning

confidence: 99%

Application of neural networks for predicting program faults

Khoshgoftaar

Pandya

Lanning³

1995

Ann Software Eng

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Accurately predicting the number of faults in program modules is a major problem in the quality control of large software development efforts. Some software complexity metrics are closely related to the distribution of faults across program modules. Using these relationships, software engineers develop models that provide early estimates of quality metrics that do not become available until late in the development cycle. By considering these early estimates, software engineers can take actions to avoid or prepare for emerging quality problems. Most often, the predictive models are based upon multiple regression analysis. However, measures of software quality and complexity exhibit systematic departures from the assumptions of these analyses. With extreme violations of these assumptions, multiple regression models become unstable and lose most of their predictive quality. Since neural network models carry no data assumptions, these models could be more appropriate than regression models for modeling software faults. In this paper, we explore a neural network methodology for developing models that predict the number of faults in program modules. We apply this methodology to develop neural network models based upon data collected during the development of two commercial software systems. After developing neural network models, we apply multiple linear regression methods to develop regression models on the same data. For the data sets considered, the neural network methodology produced better predictive models in terms of both quality of fit and predictive quality.

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“…Source lines of code (SLOC) is frequently used for estimating resources needed and should be readily available in most development environments [6,11,12,13]. In this study, lines containing only white space and lines consisting of comment characters without any alphabetic characters were not counted.…”

Section: Model Validationmentioning

confidence: 99%

A Software Implementation Progress Model

Towell

Denton

2006

Fundamental Approaches to Software Engineering

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Abstract. Software project managers use a variety of informal methods to track the progress of development and refine project schedules. Previous formal techniques have generally assumed a constant implementation pace. This is at odds with the experience and intuition of many project managers. We present a simple model for charting the pace of software development and helping managers understand the changing implementation pace of a project. The model was validated against data collected from the implementation of several large projects.

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An experimental investigation of software metrics and their relationship to software development effort

Cited by 83 publications

References 7 publications

Software complexity and software maintenance: A survey of empirical research

Software complexity and software maintenance: A survey of empirical research

Application of neural networks for predicting program faults

A Software Implementation Progress Model

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