Do you have a process in place to analyze defects, identify the defect categories and common pitfalls, and correlate the results to recommended corrective actions? Forced to get more done with less, organizations are increasingly finding themselves in need of an effective defect analysis process. David Oddis describes a systematic defect analysis process to optimize your efforts and enable higher quality software development. David’s approach promotes collaboration in the post-deployment retrospectives performed by the development/test teams. Join David as he facilitates an open conversation and provides guidance and tips via a real world walkthrough of the strategy and process he employs to analyze defects. Learn how these findings can lead to opportunities for process improvements in your requirements, design, development, test, and environment domains.

This article explains methods to build a team that will embrace "required work" and deliver robust software in a predictable fashion. It proposes a measure that helps calculate the throughput of an agile team by comparing work committed to work actually done.

During the past ten years, static analysis tools have become a vital part of software development for many organizations. However, the question arises, "Can we quantify the benefits of static analysis?" William Oliver presents the results of a study performed at Lawrence Livermore National Laboratory to do just that. They measured the cost of finding software defects using formal testing on a system without static analysis; then, they integrated a static analysis tool into the process and, over a period of time, recalculated the cost of finding software defects. Join William as he reveals the results of their study, and discusses the value and benefits of static testing with tools. Learn how commercial and open source analysis tools can perform sophisticated, interprocedural source code analysis over large code bases.

Have you heard any of these from your development staff or said them yourself? "Our software and systems are too fragile." "Technical debt is killing us." "We need more time to refactor." Having quality code is great, but we should understand why it matters and specifically what is important to your situation. Joel Tosi begins by defining and discussing some common code metrics-code complexity, coverage, object distance, afferent/efferent coupling, and cohesion. From there, Joel takes you through an application with poor code metrics and shows how this application would be difficult to enhance and extend in the future. Joel wraps up with a discussion about what metrics are applicable for specific situations such as legacy applications, prototypes, and startups. You'll come away from this class with a better understanding of code metrics and how to apply them pragmatically.