QA

Conference Presentations

Problem Resolution Cycle Time Optimization

No matter how well we plan and execute software development, defects are generated and can escape to the customers. Failure to quickly resolve software problems leads to negative consequences for our customers and increases internal business costs. A quick deterministic
method to prioritize problems and implement their solution helps to reduce cycle time and costs. Achieving this goal requires several steps. The first is to determine a model that links problem resolution performance to institutional variables and problem characteristics. Statistical Design of Experiments (DOE) is a tool that provides data requirements for estimating the impacts of these variables on problem resolution. Once data has been gathered the results of statistical analysis can be input into a mathematical optimization model to guide the organization.
This paper describes such an analysis.

Don Porter, Motorola
SM/ASM 2002: The Business Case for Software Quality

Each generation of technology-from mainframe to the Internet-creates many opportunities for businesses to try new things. But with uncharted territory comes exponentially increased risks. One way to reduce risk is to implement effective software quality processes. However, the investment required to improve development and testing infrastructures can be significant. Richard Bender addresses fourteen major areas of opportunity that underscore why this investment is critical if an organization is to succeed. He covers areas such as increasing project failure rate, the limited supply of software professionals, rising support costs, and the implications of eCommerce.

Richard Bender, Bender & Associates
Software Improvement Feedback Loops: How to Develop a Learning Organization

Over the past twenty-five years, the Software Engineering Laboratory (SEL) at NASA/GSFC has studied mechanisms for improving the software process and product. During this time, a set of processes has evolved: the Goal/Question/Metric Paradigm; the Quality Improvement Paradigm; and the Experience Factory Organization. These processes are based on the needs for measurement and feedback loops, from project to process and project to project, creating a learning organization for building software competencies. In this presentation, Victor Basili discusses these processes, how they evolved, the lessons learned, and the effects of their application in the SEL.

Victor Basili, Software Engineering Laboratory
Better Testing-Worse Quality?

Many organizations react to quality issues encountered after shipping a product by renewing their emphasis on testing. The logic is that better testing would have resulted in better software. Ironically, focusing on testing can cause worse quality. In this discussion, Elisabeth Hendrickson provides real-world examples of when better testing has resulted in worse quality and how to turn around the downward spiral.

Elisabeth Hendrickson, Quality Tree Software, Inc.
Process Improvement in Large Organizations: Walking the Maze

Every problem-solving activity in an organization requires some learning. Both the stakeholders and the
facilitators of the organization need to learn as they go through change. This paper describes our first
experiences with facilitating process improvement and problem solving by focusing on congruence, building trust
relationships, systems thinking and lots of "letting go."

Nynke Fokma, Moebius Consultancy and Erwin van der Bij, Lucent Technologies
The Software Organization as a Complex Adaptive System

We are living and working in the "knowledge rea" where business, technological, and organizational changes cannot be predicted or foreseen. One minute you are on top; the next minute, you are obsolete. For a software organization to sustain itself over time, it must act as a Complex Adaptive system (CAS) and operate on the edge of Chaos (EOC) as a learning organization that is continuously learning and co-evolving. Gain insight into how to become a sustainable software organization in a rapidly changing environment.

Nir Merry, Applied Materials and Dr. John Bruckman, Change Management Group
A Metrics Dashboard for IT Project Reporting

Tom Olenick described the activities performed to design, develop, deploy, and maintain a Project Management Metrics Dashboard across the IT organization of a major Chicago-based securities organization. Learn how this metrics dashboard was used to facilitate project status tracking for IT management and to provide a basis for improving the efficiencies of software development activities and estimation.

Thomas Olenick, Olenick & Associates
Warp 6, Mr. Sulu: The Future of Software Development

As a manager, you have many readings which help you monitor your course. Choosing a direction is a different matter. Like the crew of any Star Trek episode, you may have to make a decision based on the unknown. The best choice may not be based on what you know-but what is possible. For years we have built software in roughly the same way. Becky Winant discusses an emerging new discipline sitting on the horizon-complete with higher-level communication tools, practical knowledge capture, and advanced simulation software-that is poised and ready to change the face of software development as we know it.

Becky Winant, Esprit Systems Consulting, Inc.
Extreme Programming and CMM

This presentation explains the Capability Maturity Model and Extreme Programming, compares the two, and shows how they can be compatible.

Mark Paulk, Software Engineering Institute
Using Statistics to Evaluate Process Improvement

The techniques associated with Statistical Process Control (SPC) are very useful, but they are not sufficient alone to
provide inferential comparisons. An additional need is the ability to make valid
comparisons.
This paper suggests two additional techniques to help evaluate differences: inference for
difference between two means (using t tests and confidence intervals for the difference
between the means), and inference for two way tables (using chi-square tests). These basic
statistical techniques should be in our analysis toolbox, along with the traditional SPC tools.

Paul Below, EDS

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