THE BUSINESS CASE FOR AUTOMATING SOFTWARE METRICS IN OBJECT-ORIENTED COMPUTER AIDED SOFTWARE ENGINEERING ENVIRONMENTS

This paper makes the business case for automating the collection of software metrics for gauging development performance in integrated computer aided software engineering (CASE) environments that are characterized by an object-oriented development methodology and a centralized repository. The automation of function point analysis is discussed in the context of such an integrated CASE environment (ICE). We also discuss new metrics that describe three different dimensions of code reuse -- leverage, value and classification -- and examine the p,ossibility of utilizing objects as means to estimate software development labor and measure productivity. We argue that the automated collection of these software metrics opens up new avenues for refining the management of software development projects and controlling stra-egic costs.Information Systems Working Papers Serie

TRACKING THE 'LIFE CYCLE TRAJECTORY': METRICS AND MEASURES FOR CONTROLLING PRODUCTIVITY OF COMPUTER AIDED SOFTWARE ENGINEERING (CASE) DEVELOPMENT

This paper proposes a new vision for the measurement and management of development productivity related to computer aided software engineering (CASE) technology. We propose that productivity be monitored and controlled in each phase of software development life cycle, a measurement approach we have termed life cycle trajectory measurement. Recent advances in CASE technology that make low cost automated measurement possible have made it feasible to collect life cycle trajectory measures. We suggest that current approaches for productivity management involve the use of static metrics that are available only at the beginning and end of the project. Yet the depth of the insights needed to make proactive adjustments in the software development process requires monitoring the range of activities across the entire software development life cycle. This can only be accomplished with metrics that can measure performance parameters in each phase of the life cycle. We develop metrics that have the ability to measure and estimate software outputs from each intermediate phase of the development life cycle. These metrics are based on a count of the objects and modules that are used as building blocks for application development in repository object-based CASE environments. The viability of such object-based metrics for life cycle trajectory measurement has been empirically tested for the software construction phase using project data generated in Integrated CASE development environments.Information Systems Working Papers Serie

AUTOMATED SOFTWARE METRICS, REPOSITORY EVALUATION AND SOFTWARE ASSET MANAGEMENT: NEW TOOLS AND PERSPECTIVES FOR MANAGING INTEGRATED COMPUTER AIDED SOFTWARE ENGINEERING (I-CASE)

Automated collection of software metrics in computer aided software engineering (CASE) environments opens up new avenues for improving the management of software development operations, as well as shifting the focus of management's control efforts from "software projectâ to "software assets" stored in a centralized repository. Repository evaluation, a new direction for software metrics research in the 1990s, promises a fresh view of software development performance for a range of responsibility levels. We discuss the automation of function point and code reuse analysis in the context of an integrated CASE (I-CASE) environment deployed at a large investment bank in New York City. The development of an automated code reuse analysis tool prompted us to re-think how to measure and interpret code reuse in the I-CASE environment. The metrics we propose describe three dimensions of code reuse -- leverage, value and classification -- and we examine the value of applying them on a project and a repository-wide basis.Information Systems Working Papers Serie

Measuring usability for application software using the quality in use integration measurement model

User interfaces of application software are designed to make user interaction as efficient and as simple as possible. Market accessibility of any application software is determined by the usability of its user interfaces. A poorly designed user interface will have little value no matter how powerful the program is. Thus, it is significantly important to measure usability during the system development lifecycle in order to avoid user disappointment. Various methods and standards that help measure usability have been developed. However, these methods define usability inconsistently, which makes software engineers hesitant in implementing these methods or standards. The Quality in Use Integrated Measurement (QUIM) model is a consolidated approach for measuring usability through 10 factors, 26 criteria, and 127 metrics. It decomposes usability into factors, criteria, and metrics, and it is a hierarchical model that helps developers with no or little background of usability metrics. Among 127 metrics of QUIM, essential efficiency (EE) is the most specific metric used to measure the usability of user interfaces through an equation. This study involves a comparative analysis between three case studies that use the QUIM model to measure usability in terms of EE for three case studies: (1) Public University Registration System, (2) Restaurant Menu Ordering System, and (3) ATM system. A comparison is made based on the percentage of EE for each element of the use cases in each use case diagram. The results obtained revealed that the user interface design for Restaurant Menu Ordering System scored the highest percentage of EE, thus proving to be the most user-friendly application software among its counterparts

Classification of software components based on clustering

This thesis demonstrates how in different phases of the software life cycle, software components that have similar software metrics can be grouped into homogeneous clusters. We use multi-variate analysis techniques to group similar software components. The results were applied on several real case studies from NASA and open source software. We obtained process and product related metrics during the requirements specification, product related metrics at the architectural level and code metrics from operational stage for several case studies. We implemented clustering analysis using these metrics and validated the results. This analysis makes it possible to rank the clusters and assign similar development and validation tasks for all the components in a cluster, as the components in a cluster have similar metrics and hence tend to behave alike

AUTOMATING OUTPUT SIZE AND REUSABILITY METRICS IN AN OBJECT-BASED COMPUTER AIDED SOFTWARE ENGINEERING (CASE) ENVIRONMENT

Measurement of software development productivity is needed in order to control software costs, but it is discouragingly labor-intensive and expensive. Computer aided software engineering (CASE) technologies -- especially object-oriented, integrated CASE -- have the potential to support the automation of this measurement. In this paper, we discuss the conceptual development of automated analyzers for function point and software reusability measurement for object-based CASE. Both analyzers take advantage of the existence of a representation of the application system that is stored within an object repository, and that contains the necessary information about the application system. We also propose new metrics for software reusability measurement, including reuse leverage, reuse value and reuse classification. The functionality and analytic capabilities of state-of-the-art automated software metrics analyzers are illustrated in the context of an investment banking industry application.Information Systems Working Papers Serie

Assembling a Metrics Suite for Rule-Based Systems Development

Metrics have been routinely employed in the development of traditional software. With the adoption of the Capability Maturity ModelSM from SEI, the formal incorporation of metrics into the development process is mandated for higher levels of development practice. In the case of knowledge-based systems (KBS), very little use of formal metrics is reported. This paper examines some of the reasons for this situation, and provides a suite of metrics that can be employed in the development of KBS

High-Dimensional Software Engineering Data and Feature Selection

Software metrics collected during project development play a critical role in software quality assurance. A software practitioner is very keen on learning which software metrics to focus on for software quality prediction. While a concise set of software metrics is often desired, a typical project collects a very large number of metrics. Minimal attention has been devoted to finding the minimum set of software metrics that have the same predictive capability as a larger set of metrics – we strive to answer that question in this paper. We present a comprehensive comparison between seven commonly-used filter-based feature ranking techniques (FRT) and our proposed hybrid feature selection (HFS) technique. Our case study consists of a very highdimensional (42 software attributes) software measurement data set obtained from a large telecommunications system. The empirical analysis indicates that HFS performs better than FRT; however, the Kolmogorov-Smirnov feature ranking technique demonstrates competitive performance. For the telecommunications system, it is found that only 10% of the software attributes are sufficient for effective software quality prediction

Empirical Validation of the Usefulness of Information Theory-Based Software Metrics

Software designs consist of software components and their relationships. Graphs are abstraction of software designs. Graphs composed of nodes and hyperedges are attractive for depicting software designs. Measurement of abstractions quantify relationships that exist among components. Most conventional metrics are based on counting. In contrast, this work adopts information theory because design decisions are information. The goal of this research is to show that information theory-based metrics proposed by Allen, namely size, complexity, coupling, and cohesion, can be useful in real-world software development projects, compared to the counting-based metrics. The thesis includes three case studies with the use of global variables as the abstraction. It is observed that one can use the counting metrics for the size and coupling measures and the information metrics for the complexity and cohesion measures