In a world with highly competitive markets, there is a great need in almost all business organizations to develop a highly effective coordination and decision support tool that can be used to become a daily life predictive enterprise to direct, optimize and automate specific decision-making processes. The improved decision-making support can help people to examine data on the past circumstances and present events, as well as project future actions, which will continually improve the quality of products or services. Such improvement has been driven by recent advances in digital data collection and storage technology. The new technology in data collection has resulted in the growth of massive databases, also known as data avalanches. These rapidly growing databases occur in various applications including service industry, global supply chain organizations, air traffic control, nuclear reactors, aircraft fly-by-wire, real time sensor networks, industrial process control, hospital healthcare, and security systems. The massive data, especially text records, on one hand, may contain a great wealth of knowledge and information, but on the other hand, contain other information that may not be reliable due to many uncertainty reasons in our changing environments. However, manually classifying thousands of text records according to their contents can be demanding and overwhelming. Data mining has gained a lot of attention from researchers and practitioners over the past decade as an emerging research area in finding meaningful patterns to make sense out of massive data sets.

Regression testing has been conventionally employed to check the effectiveness of a solution, track existing issues and any new issues created by the result of fixing the old issues. Positioned at the tail end of the software cycle, regression testing technology can hardly influence or contribute to earlier phases such as architect, design, implementation or device testing. Extending the "R" in ART to R4 (regression, research, retain & grow expertise and early exposure) has been proving. R4 is not only providing ART with more powerful tools to detect issues as early as in the architect phase, but also arming R&D software with more proactive practices to avoid costly catastrophic problems from propagating to customer sites. This paper attempts to share some best practices and contributions from Cisco-ARF (a Cisco automated regression/research facility) whose charter is to ensure the quality of product lines running on tens of million lines of code. These award-winning practices have proven to save multi-million dollars in repair costs, thousands of engineering hours, and continue to set the higher standards for testing technology under proactive leadership and management to gain higher quality and customer satisfaction.

Software memory leakage accounts for many dynamic system problems ranging from minor performance deterioration to major system crash due to low memory, security exploitation or other side effects. General purpose commercial static and dynamic memory leak analysis tools are available for common operating systems. However, these tools normally produce high noise ratio of warning messages that require many human hours to review and eliminate false-positive alarms. In-house tools for proprietary platforms with special memory architectures also face the same limitation. Human memory on the parallel path has been studied by neuroscientists and well documented along with the governing behavioral mathematic expressions. Some studies from neuroscience inspired us towards a new approach to resolve the software memory leak issues that were occurring in our proprietary operating system. The results of our study and experiment not only allowed us to create a method to accurately detect memory leaks as a starting point, but also laid out a roadmap for future work in this area by applying the neuroscience findings into computer software to detect and control the system resources. We hope our findings and experience will help others to decrease the effort of fighting against system memory leak, whether starting from scratch, or as a reference to improve the existing tools to reduce the reporting noise ratio. In this paper, we will walk through our mapping of Cue, Recognition and Recall used in Kahana's neuroscience method [2000] to the similar memory elements of our target operating system, and how we applied Yule's Q equation to accurately pinpoint the memory leak in our source code and how we continuously fine tune the noise threshold. Our immediate road map shows a mathematic model to predict the system memory resource behavior and how we will apply it to our memory leak detection tool to help prolong system availability.