Halstead Metrics Research Articles

Ex Ante Evaluations of Alternate Data Structures for End User Queries

The data structure of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. This research develops a methodology for evaluating, ex ante, the relative desirability of alternative data structures for end user queries. This research theorizes that the data structure that yields the lowest weighted average complexity for a representative sample of information requests is the most desirable data structure for end user queries. The theory was tested in an experiment that compared queries from two different relational database schemas. As theorized, end users querying the data structure associated with the less complex queries performed better. Complexity was measured using three different Halstead metrics. Each of the three metrics provided excellent predictions of end user performance. This research supplies strong evidence that organizations can use complexity metrics to evaluate, ex ante, the desirability of alternate data structures. Organizations can use these evaluations to enhance the efficient and effective retrieval of information by creating data structures that minimize end user query complexity.

Software metrics: good, bad and missing

The software industry is an embarrassment when it comes to measurement and metrics. Many software managers and practitioners, including tenured academics in software engineering and computer science, seem to know little or nothing about these topics. Many of the measurements found in the software literature are not used with enough precision to replicate the author's findings-a canon of scientific writing in other fields. Several of the most widely used software metrics have been proved unworkable, yet they continue to show up in books, encyclopedias, and refereed journals. So long as these invalid metrics are used carelessly, there can be no true software engineering, only a kind of amateurish craft that uses rough approximations instead of precise measurement. The paper considers three significant and widely used software metrics that are invalid under various conditions: lines of code or LOC metrics, software science or Halstead metrics, and the cost-per-defect metric. Fortunately, two metrics that actually generate useful information-complexity metrics and function-point metrics-are growing in use and importance.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Alternative approaches for the use of metrics to order programs by complexity

With many program complexity metrics available, it is difficult to rank programs by complexity: the different metrics can give different indications. There are two parts to this problem. First, because different metrics can measure the same program attribute, we need a method of evaluating a given program attribute based on the values of all metrics that measure this attribute. Second, because different metrics can measure distinct program attributes, we need a method of evaluating the overall program complexity based on the values of all program attributes. This article compares two methods of simultaneously detecting those aspects of software complexity measured by the Halstead metrics and the McCabe cyclomatic complexity number. The first method synthesizes a combined metric by weighting each Halstead metric to reflect program attributes measured by the cyclomatic complexity number. The second method uses principal components analysis. This method derives a relative complexity metric, which represents each complexity metric in proportion to the amount of unique variation that it contributes. A validation study establishes a useful statistical relationship between this relative complexity metric and faults for a military telecommunications development effort.

Statistical methods for the analysis of software metrics data

The authors present case study applications of statistical methods for the analysis of software metrics data which recognize the discrete nature of such data. A procedure is also described which allows a component of complexity independent of size to be extracted from the usual Halstead's metrics and McCabe's cyclomatic number. The methods described are different from the usual regression and non-parametric methods previously applied to software metrics. With the software quality practitioner in mind, the paper explores how these new methods are helpful in understanding the relationships between software metrics.

On the resolution of the software science anomaly

In a recent article 1. [1], Card and Agresti offered empirical and analytical evidence to refute the use of the Halstead length estimator as an approximation to actual program length. The purpose of their paper was to resolve the so-called anomaly between the lack of theoretical foundation for the Halstead metrics and the empirical support that other researchers have provided for these metrics. One major part of their analysis resulted in the conclusion that the actual and estimated length equations are inherently dependent on one another. It is shown that this conclusion is incorrect, and that some of the subsequent observations relevant to this conclusion are inaccurate 2. [2]. It is also argued that, since several researchers have provided empirical evidence that the length estimator does not give good results when applied to large modules, there may really have been no anomaly to begin with.

Computer Program Plagiarism Detection: The Limits of the Halstead Metric

Plagiarism detection is an increasingly important issue in educational environments. This article employs two alternative metrics in the identification of program similarity. The first is the Halstead metric drawn from the discipline of software science. The second is an ad hoc metric drawn from program grading experience, and identified by means of factor analysis. The ad hoc metric proves to be more useful in identical-task environments. Possible explanations for, as well as some larger implications of, this result are considered.

An Experimental Study of Software Metrics for Real-Time Software

The rising costs of software development and maintenance have naturally aroused intere5t in tools and measures to quantify and analyze software complexity. Many software metrics have been studied widely because of the potential usefulness in predicting the complexity and quality of software. Most of the work reported in this area has been related to nonreal-time software. In this paper we report and discuss the results of an experimental investigation of some important metrics and their relationship for a class of 202 Pascal programs used in a real-time distributed processing environment. While some of our observations confirm independent studies, we have noted significant differences. For instance the correlations between McCabe's control complexity measure and Halstead's metrics are low in comparison to a previous study. Studies of the type reported here are important for understanding the relationship between software metrics.

Complexity measurement of Electronic Switching System (ESS) software

We have been developing a tool that measures the complexity of software: 1) to predict the quality of software products and 2) to allocate proportionally more testing resources to complex modules. The software being measured is real-time and controls telephone switching systems. This software system is large and its development is distributed over a period of several years, with each release providing enhancements and bug fixes. We have developed a two-stage tool consisting of a parser and an analyzer. The parser operates on the source code and produces operator, operand, and miscellaneous tables. These tables are then processed by an analyzer program that calculates the complexity measures. Changes for tuning our Halstead counting rules involve simple changes to the analyzer only. During the development there were problems and issues to be confronted dealing with static analysis and code metrics. These are also described in this paper. In several systems we found that more than 80% of software failures can be traced to only 20% of the modules in the system. The McCabe complexity and some of Halstead's metrics score higher than the count of executable statements in their correlations with field failures. It is reasonable to expect that we could devote more effort to the review and test of high-complexity modules and increase the quality of the software product that we send to the field.

An application of software science to the quantitative measurement of code quality

The error rate of a software application may function as a measure of code quality. A methodology has been developed which allows for the accurate prediction of the error rate and hence code quality prior to an application's release. Many factors were considered which could conceivably be related to the error rate. These factors were divided into two categories: those factors which vary with time, and those factors which do not vary with time. Factors which vary with time were termed environmental factors and included such items as: number of users, errors submitted to date, etc. Factors which do not vary with time were termed internal factors and included Halstead metrics, McCabe metrics and lines of code.

A software study using Halstead metrics

This paper describes an application of Maurice Halstead's software theory to a real time switching system. The Halstead metrics and the software tool developed for computing them are discussed. Analysis of the metric data indicates that the level of the switching language was not constant across algorithma and that software error data was not a linear function of volume.