This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Given the central role that software development plays in the delivery and application of information technology, managers are increasingly focusing on process improvement in the software development area. This demand has spurred the provision of a number of new and/or improved approaches to software development, with perhaps the most prominent being object-orientation (OO). In addition, the focus on process improvement has increased the demand for software measures, or metrics with which to manage the process. The need for such metrics is particularly acute when an organization is adopting a new technology for which established practices have yet to be developed. This research addresses these needs through the development and implementation of a new suite of metrics for OO design. Metrics developed in previous research, while contributing to the field's understanding of software development processes, have generally been subject to serious criticisms, including the lack of a theoretical base. Following Wand and Weber (1989), the theoretical base chosen for the metrics was the ontology of Bunge (1977). Six design metrics are developed, and then analytically evaluated against Weyuker's (1988) proposed set of measurement principles. An automated data collection tool was then developed and implemented to collect an empirical sample of these metrics at two field sites in order to demonstrate their feasibility and suggest ways in which managers may use these metrics for process improvement. >

/pdf/a-metrics-suite-for-object-oriented-design-539bycjzzb.pdf

A metrics suite for object oriented design

https://csse.usc.edu/TECHRPTS/1984/usccse84-500/usccse84-500.pdf

Software Engineering Economics

Program slicing is a method for automatically decomposing programs by analyzing their data flow and control flow. Starting from a subset of a program's behavior, slicing reduces that program to a minimal form which still produces that behavior. The reduced program, called a ``slice,'' is an independent program guaranteed to represent faithfully the original program within the domain of the specified subset of behavior. Some properties of slices are presented. In particular, finding statement-minimal slices is in general unsolvable, but using data flow analysis is sufficient to find approximate slices. Potential applications include automatic slicing tools for debuggng and parallel processing of slices.

Program Slicing

Experience from a dozen years of analyzing software engineering processes and products is summarized as a set of software engineering and measurement principles that argue for software engineering process models that integrate sound planning and analysis into the construction process. In the TAME (Tailoring A Measurement Environment) project at the University of Maryland, such an improvement-oriented software engineering process model was developed that uses the goal/question/metric paradigm to integrate the constructive and analytic aspects of software development. The model provides a mechanism for formalizing the characterization and planning tasks, controlling and improving projects based on quantitative analysis, learning in a deeper and more systematic way about the software process and product, and feeding the appropriate experience back into the current and future projects. The TAME system is an instantiation of the TAME software engineering process model as an ISEE (integrated software engineering environment). The first in a series of TAME system prototypes has been developed. An assessment of experience with this first limited prototype is presented including a reassessment of its initial architecture. >

/pdf/the-tame-project-towards-improvement-oriented-software-42akqq9cmq.pdf

The TAME project: towards improvement-oriented software environments

This paper describes a graph-theoretic complexity measure and illustrates how it can be used to manage and control program complexity. The paper first explains how the graph theory concepts apply and gives an intuitive explanation of the graph concepts in programming terms. The control graphs of several actual FORTRAN programs are then presented to illustrate the correlation between intuitive complexity and the graph theoretic complexity. Several properties of the graph-theoretic complexity are then proved which show, for example, that complexity is independent of physical size (adding or subtracting functional statements leaves complexity unchanged) and complexity depends only on the decision structure of a program.The issue of using non-structured control flow is also discussed. A characterization of non-structured control graphs is given and a method of measuring the “structuredness” of a program is developed. The relationship between structure and reducibility is illustrated with several examples.The last section of the paper deals with a testing methodology used in conjunction with the complexity measure; a testing strategy is defined that dictates that a program can either admit of a certain minimal testing level or the program can be structurally reduced.

A complexity measure

This paper describes a graph-theoretic complexity measure and illustrates how it can be used to manage and control program complexity. The paper first explains how the graph-theory concepts apply and gives an intuitive explanation of the graph concepts in programming terms. The control graphs of several actual Fortran programs are then presented to illustrate the correlation between intuitive complexity and the graph-theoretic complexity. Several properties of the graph-theoretic complexity are then proved which show, for example, that complexity is independent of physical size (adding or subtracting functional statements leaves complexity unchanged) and complexity depends only on the decision structure of a program.

Thomas J. McCabe

Papers

A complexity measure

A Complexity Measure

A complexity measure

A Complexity Measure (Abstract).