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2007 Awarded the Cisco company title “White Hat Hacker of the year”
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Patents 7, 765, 174; 7, 293, 142; 7, 721, 265; 7, 930, 491; 6, 999, 952; US6999952, US20060107153, US 07293142, US 7293142 B1, US 20060107153 A1
Network Vulnerability From Memory Abuse and Experimented Software Defect Detection
Jun Xu Christopher Hoang Pham
ISSRE 2002, 13th IEEE International Symposium on Software Reliability Engineering (ISSRE 2003); Annapolis, Maryland; Nov. 12-15, 2002; Chillarege Press; Copyright 2002
While majority of software developers are concerning about features, performance, CPU usage and similar criteria, many neglects memory management as one of the most fundamental resources for software operation. The consequence of this negligence is more severe than it sounds, such as: the memory resource can be exhausted by malicious applications leading to system malfunction, and the most vulnerable of all is the risk of security attack.
To address some of the observed common run-time problems resulted from poor memory management, the authors developed tools to detect the problems early in the development cycle and isolated them to source code level. A practice was also experimented to alleviate the poor memory management software defects in important phases of the SEI software development model as a solution.
This paper shares with the reader the non-proprietary observed data, methods and technology that was developed and leveraged to address some severe memory abuse issues in both off-line and run-time domains.
Less Intrusive Memory Leak Detection inside Kernel
Jun Xu , Xiangrong Wang , Christopher Pham
Fast Abstract ISSRE 2003); 14th IEEE International Symposium on Software Reliability Engineering (ISSRE 2003); Denver, Colorado; Nov. 17-20, 2003; Chillarege Press; Copyright 2003 (2 pages).
Memory leak is a major resource issue which could lead to many system malfunctions and negative performance impacts. A memory leak occurs when memory is not freed after use, or when the pointer to a memory allocation is deleted, rendering the memory no longer usable. It can exhibit in many forms, contiguously or fragmentally, in flatten memory architecture or those with virtual space. Reckless use of dynamic memory allocation can lead to memory management problems, which cause performance degradation, unpredictable execution or crashes.
Memory leak detection system and method using contingency analysis
United States Patent 7293142, http://www.freepatentsonline.com/7293142.html
Xu, Jun (Cupertino, CA, US) , Wang, Xiangrong (Milpitas, CA, US) , Pham, Christopher H. (Milpitas, CA, US) , Goli, Srinivas (San Jose, CA, US)
The present invention relates to testing of hardware and software, and particularly to the detection and identification of memory leaks in software.
An effective method to detect software memory leakage leveraged from neuroscience principles governing human memory behavior
Published in: Software Reliability Engineering, 2004. ISSRE 2004. 15th International Symposium; Page(s):329 - 339, Print ISBN:0-7695-2215-7
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  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 availabilitty.
Linear associative memory-based hardware architecture for fault tolerant ASIC/FPGA work-around
United States Patent 6999952, http://www.freepatentsonline.com/6999952.html
A programmable logic unit (e.g., an ASIC or FPGA) having a feedforward linear associative memory (LAM) neural network checking circuit which classifies input vectors to a faulty hardware block as either good or not good and, when a new input vector is classified as not good, blocks a corresponding output vector of the faulty hardware block, enables a software work-around for the new input vector, and accepts the software work-around input as the output vector of the programmable logic circuit. The feedforward LAM neural network checking circuit has a weight matrix whose elements are based on a set of known bad input vectors for said faulty hardware block. The feedforward LAM neural network checking circuit may update the weight matrix online using one or more additional bad input vectors. A discrete Hopfield algorithm is used to calculate the weight matrix W. The feedforward LAM neural network checking circuit calculates an output vector a(m) by multiplying the weight matrix W by the new input vector b(m), that is, a(m)=wb(m), adjusts elements of the output vector a(m) by respective thresholds, and processes the elements using a plurality of non-linear units to provide an output of 1 when a given adjusted element is positive, and provide an output of 0 when a given adjusted element is not positive. If a vector constructed of the outputs of these non-linear units matches with an entry in a content-addressable memory (CAM) storing the set of known bad vectors (a CAM hit), then the new input vector is classified as not good.
Performing high efficiency source code static analysis with intelligent extensions
Published in: Software Engineering Conference, 2004. 11th Asia-Pacific, Page(s):346 - 355, Print ISBN:0-7695-2245-9
This paper presents an industry practice for highly efficient source code analysis to promote software quality. As a continuous work of previously reported source code analysis system, we researched and developed a few engineering-oriented intelligent extensions to implement more cost-effective extended code static analysis and engineering processes. These include an integrated empirical scan and filtering tool for highly accurate noise reduction, and a new code checking test tool to detect function call mismatch problems, which may lead to many severe software defects. We also extended the system with an automated defect filing and verification procedure. The results show that, for a huge code base of millions of lines, our intelligent extensions not only contribute to the completeness and effectiveness of static analysis, but also establish significant engineering productivity.
Recent Advances in Data Mining for Categorizing Text Records
W. Chaovalitwongse, Hoang Pham, Seheon Hwang, Z. Liang, C.H. Pham
Book Title Recent Advances in Reliability and Quality in Design, Book Part V, Pages 423-440, Print ISBN978-1-84800-112-1
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.
An Effective Low Cost Whitebox Approach to Construct System Level Test Vectors to Detect Buffer Overflow Defects
Fast Abstract ISSRE 2003, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.147.2339&rep=rep1&type=pdf
Buffer overflow continues to lead the list of 60% of the recent 2002 CERT advisories. In line with the effort to solve this problem, the paper shares a low cost method as has been used effectively to construct the test vectors to detect online buffer overflow software defects.
Published in: Engineering Management Conference, 2006 IEEE International, Page(s):94 - 100, Print ISBN:1-4244-0285-9
In IEMC 2004 publication, the paper "Using Sport Analogy in High-Tech Management to Improve Productivity by Improving Personal and Team Performance"  received amazing number of feedback. Further communication with readers inspires the authors to continue with this contribution to provide more insight of our coaching and development process to build, support and continually renew the team. The paper describes the management strategy to prepare, win and sustain the global high-tech games with a team composed of high performers with complement skill sets and experience from all regions of the so-call "flat world". Again, sport analogy is used in the same style as the first paper to deliver the points across.
Using sport analogy in high-tech management to improve productivity by improving personal and team performance
K. Houshmand ; ARF, Cisco Syst., Inc., San Jose, CA, USA ; S. GoIi ; R. Esmaili ; C. H. Pham
Published in: Engineering Management Conference, 2004. Proceedings. 2004 IEEE International (Volume:1 ), Page(s):11 - 15 Vol.1Print ISBN:0-7803-8519-5
Improve personal and team performance is the main focus of sports management. Many similarities exist in the high tech industry with high demand for creativity and higher productivity, which translates into higher personal and team performance. We would like to share our experience from our management ABC-GOAL-FIRST strategy and S-I-R business operation model that we used from 2001 to 2004. The creative strategy which focuses on personal and team performance improvement based on the same analogy of sports management has helped expand our team's charter from a regression facility at the tail end of the software life cycle to a bigger company-wide scope. The expanded charter allows the team to be involved in all phases of the software life cycle collaboratively and cross-functionally to maximize our contribution while leveraging other organizational expertise. We also would like to share the analysis, the metrics and the positive human affect of the proven management strategy that helped to increase our team's productivity by six folds during the past four years.
Palla, M. ; Pham, C. ; Houshmand, K. ; Jose, J.P. ; Vedamoorthy, M.
Published in:Engineering Management Conference, 2006 IEEE International, Page(s):101 - 105, Print ISBN:1-4244-0285-9
In this paper we share our experience of how working together corporations can create "One Team" consisting of many virtual teams, and overcome the challenges posed by the global economy.
Turning and Managing Innovation into Automation for Higher Competitive Productivity
Palla, M. ; Cisco Syst., Inc., San Jose, CA ; Hu, B. ; Houshmand, K. ; Pham, C.
Published in: Management of Innovation and Technology, 2006 IEEE International Conference on (Volume:2 ), Page(s):1048 - 1052, Print ISBN:1-4244-0147-X
Innovation boosts productivity and drives prosperity. In the new millennium's competitive high tech environment, innovation allows companies to stay competitive in their own sectors while enabling them to advance further into additional areas. Effective leaders rely on and leverage both people skills and automated machine power to maximize the team productivity. While innovators prove the tools and best practices to boost productivity, adaptors integrate these innovations in daily life environment and realize the maximum long term gains for the company. While flexible environment prosper innovation, stable structure nurtures adaptation. There are a number of challenges that leaders have to face in order to first facilitate an environment for managed innovation, then finally automate and integrate it into the process. This paper discusses the challenges faced by the Advanced Regression/Research Facility (ARF) at Cisco Systems in leading and creating an environment that embraces changes seamlessly. In order to complement the traditional regression testing, innovation concepts were proven, automated and integrated into ARF's daily business to improve its defect finding rate. The innovations resulted in changes and introduced management challenges but paid handsome dividend in the end by improving its productivity 900% within 6 years in an information-sharing and individual-recognition environment.
Extend the meaning of "R" to "R4" in ART (automated software regression technology) to improve quality and reduce R&D and production costs
Houshmand, K. ; ARF, Cisco Syst. Inc., San Jose, CA, USA ; Goli, S. ; Esmaili, R. ; Pham, C.H.
Published in: Engineering Management Conference, 2004. Proceedings. 2004 IEEE International (Volume:1 ), Page(s):70 - 74 Vol.1, Print ISBN:0-7803-8519-5
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.
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