Alan Tsu-I Yaung

Design and implementation of a requirements clustering analyzer for software system decomposition

Requirements clustering is a rww apprc)ach to system decomposition which is an important activity in developing large and complex software systems. It enables one IO divide a system into user-recognizable components where each component can bc used to satisfy part of the user’s needs. This paper clcscribcs the underlying concept of requirements clustering, and ciiscusses the design and implementation of the Requirements Clustering Analyzer that is an automated decomposition tool supporting requirements clustering. An example is presented to demonstrate the usage of the analyzer. Iuturc directions of the research are also suggested to improve the protot ypc system.

Factors affecting design inspection effectiveness in software development

Abstract We report the results of an analysis of defect-escape data from design inspections in two maintenance releases of a large software product. We found that the less effective inspections were those with the largest time investment, the likelihood of defect escapes being clearly affected by the way in which the time was invested and by the size of the work product inspected. Regression analysis of the data resulted in logarithmic and exponential equations relating these variables to defect-escape probability. We conclude with some practical guidelines for improving inspection effectiveness.

Application of clustering techniques to information systems design

Abstract We describe the application of two variations of agglomerative clustering to the analysis of planning data for an organization-wide information system design project. One algorithm used similarity as a measure of distance, while the other is based on the extent of inclusion of an object by another. The two algorithms take into account the graduated nature of the relationships among objects while measuring the distance between them or combining them into new clusters. The paper also shows how to analyse the association between the two sets of clusters identified by the algorithms.

The Impact of Incremental Delivery on Maintenance Effort: An Analytical Study

Incremental delivery (ID) is a relatively new software development paradigm which advocates that systems be delivered to end-users in usable, useful, and semi-independent chunks (increments). ID differs from the traditional development paradigm, which we call monolithic development (MD), where a software system is considered as a monolithic, inseparable whole delivered as one unit. The purpose of this study is to compare the ID and MD approaches in terms of their maintenance costs through an analytical parametric study. The results of the study provide insight into how incremental delivery can be employed to reduce software maintenance effort and costs.

A predictive model for identifying inspections with escaped defects

Defect origin and inspection cost data pertaining to a large software development project was analyzed by using statistical techniques. First, we analyzed the difference between the inspections with escaped defects and those without escapes regarding inspection hours, preparation hours, and code size. Next, we examined the composite measures such as, total effect, preparation/inspection ratio, and effort ratio. Then, we developed a linear discriminant function to assess the effectiveness of an inspection. The results lead to several findings regarding the effect of total inspection effort, preparation time, and artifact size on the occurrence of defect escapes. The methodology used in this study can be applied to other software development organizations, and the findings can be generalized to a variety of inspection processes.<<ETX>>

Process clustering with an algorithm based on a coupling metric

Abstract Process management has received considerable attention in recent years as a means to improve productivity and quality in the software industry. Quite often, it might be advantageous to group related processes to reduce and control the amount of effort needed to measure and manage the processes in the organization. Here we present an algorithm for grouping processes into process clusters. The article begins with the development of a coupling metric that measures the strength of the linkage between processes. Next, the algorithm is presented. Finally, we report on the results obtained while analyzing the processes in a large software development organization.

Linkage analysis of processes

This paper presents a methodology for the analysis of the linkage among the processes in an organization. The methodology has three main steps: model construction; connectivity analysis; and structure analysis. The model construction step generates a linkage matrix which is used in the following steps. The connectivity analysis is based on linkage consistency and interprocess coupling metrics, which are defined for each process individually and for all the processes in the organization as a group. The clustering algorithm is based on a linkage intensity measure derived from the linkage matrix. Following a detailed description of the methodology and its metrics and algorithms, the results obtained in a medium‐size software development organization are presented.

A linkage analyzer for process management

Alan T. Yaung