Malik Jahan Khan

Enabling Self-Configuration in Autonomic Systems Using Case-Based Reasoning with Improved Efficiency

Autonomic computing is an emerging philosophy which promises to enable self-management capabilities in software systems. These self-management properties include self-configuration, self-healing, self-protection, self-optimization, self-awareness and self-governance. Enabling any of these properties in software systems is an open challenge. Exhibiting such self-management behavior is a continuous process in the software life cycle. Case-based reasoning is a problem solving methodology which exploits past experience. Past experience is maintained in the form of problem-solution pairs, also called cases. On the arrival of new problem, solution of past similar problems is used after appropriate adaptation. This problem solving technique can be used to achieve some of the properties of autonomic systems based on experience. To find this solution, entire experience space is searched which reduces efficiency. To overcome this efficiency problem, we restrict the fast growth of case repository, so that every time we have to search a very limited number of cases. We applied the proposed approach on a simulation of Autonomic Forest Fire application for self-configuration capability. Our results show that the proposed approach is quite promising in terms of accuracy as well as efficiency.

Survey of Frameworks, Architectures and Techniques in Autonomic Computing

A variety of frameworks, architectures and techniques have been proposed and used in the field of autonomic computing for self-management. There are also many applications and systems available that exhibit autonomic behavior. However all techniques and applications do not explicitly use autonomic or self-* terminologies to describe their autonomic characteristics. In this survey paper, a review of existing autonomic frameworks, architectures and self-management techniques is presented. It gives a panoramic picture of the researchfields of autonomic computing. Further analysis is done to categorize the surveyed frameworks, architectures, infrastructures and techniques.

Software quality prediction techniques: A comparative analysis

There are many software quality prediction techniques available in literature to predict software quality. However, literature lacks a comprehensive study to evaluate and compare various prediction methodologies so that quality professionals may select an appropriate predictor. To find a technique which performs better in general is an undecidable problem because behavior of a predictor also depends on many other specific factors like problem domain, nature of dataset, uncertainty in the available data etc. We have conducted an empirical survey of various software quality prediction techniques and compared their performance in terms of various evaluation metrics. In this paper, we have presented comparison of 30 techniques on two standard datasets.

Achieving Self-configuration Capability in Autonomic Systems Using Case-Based Reasoning with a New Similarity Measure

A lot of activities inside human body are carried out intelligently without the explicit intervention of human itself, e.g. various actions of nervous systems, blood circulation system etc. Inspired from these natural systems, autonomic computing is an emerging concept which promises to enable such kind of self-management capabilities inside software systems. Case-based reasoning (CBR) is a methodology to solve current problems using the solutions of past problems of the similar nature. In this paper, we propose to use CBR to achieve self-configuration in autonomic systems. We introduce a new similarity measure to find nearest neighbors. We have also suggested the case preparation, case retrieval and case reuse and refinement methods to enable self-configuration in autonomic systems. To support our proposed methodology, we illustrate a case-study of Autonomic Forest Fire Application.

Comparative Study of Various Artificial Intelligence Techniques to Predict Software Quality

Software quality prediction models are used to identify software modules that may cause potential quality problems. These models are based on various metrics available during the early stages of software development life cycle like product size, software complexity, coupling and cohesion. In this survey paper, we have compared and discussed some software quality prediction approaches based on Bayesian belief network, neural networks, fuzzy logic, support vector machine, expectation maximum likelihood algorithm and case-based reasoning. This study gives better comparative insight about these approaches, and helps to select an approach based on available resources and desired level of quality.

Self-Configuration in Autonomic Systems Using Clustered CBR Approach

Self-configuration is one of the key properties of autonomic systems. We apply an experience-based artificial intelligence approach known as case-based reasoning (CBR) in order to help autonomic manager to devise new configuration solution. Searching the entire case-base on occurrences of every new problem is a time consuming task. We propose to cluster the case-base and classify each new problem among one of the clusters. Our approach to reduce the search space promises to achieve efficiency as well as accuracy. We performed experiments on a simulation of autonomic forest fire application and achieved inspiring results.

Automatic Case Generation for Case-Based Reasoning Systems Using Genetic Algorithms

Case-Based Reasoning (CBR) has been employed as a problem-solving technique to solve numerous real-world applications. At the core of a successful CBR system is a high-quality case-base. Generating a quality case-base with minimal human intervention is a significant challenge which has not been given considerable attention in the past. In this paper, we propose a methodology for automatic generation of a quality case-base using genetic algorithm (GA). GA has been effectively used to evaluate quality of cases using predefined criteria as part of the fitness function. The performance and efficiency of the proposed approach has been evaluated and presented on the examination scheduling problem.

An empirical study of modeling self-management capabilities in autonomic systems using case-based reasoning

Abstract Autonomic systems promise to inject self-managing capabilities in software systems. The major objectives of autonomic computing are to minimize human intervention and to enable a seamless self-adaptive behavior in the software systems. To achieve self-managing behavior, various methods have been exploited in past. Case-based reasoning (CBR) is a problem solving paradigm of artificial intelligence which exploits past experience, stored in the form of problem–solution pairs. We have applied CBR based modeling approach to achieve autonomicity in software systems. The proposed algorithms have been described and CBR implementation on externalization and internalization architectures of autonomic systems using two case studies RUBiS and Autonomic Forest Fire Application (AFFA) have been shown. The study highlights the effect of 10 different similarity measures, the role of adaptation and the effect of changing nearest neighborhood cardinality for a CBR solution cycle in autonomic managers. The results presented in this paper show that the proposed CBR based autonomic model exhibits 90–98% accuracy in diagnosing the problem and planning the solution.

Prediction and Analysis of Air Incidents and Accidents Using Case-Based Reasoning

Prediction of upcoming events has very critical role in many disciplines of life. Air accidents and incidents are one of such critical events. There are many existing learning methods in literature. Case-based reasoning (CBR) is a lazy learning technique of artificial intelligence which exploits past experience very efficiently. It works well when precise information is not available and available information is not well-structured. In this paper, we propose to apply CBR for prediction of air accidents and incidents. In the proposed framework, we describe the retrieval strategies, solution algorithms and revision mechanism. We have implemented the proposed idea for the data of air accidents, incidents and crashes. The results show that up to 87% accuracy can be achieved using the proposed framework.

Blending Six Sigma and CMMI - an approach to accelerate process improvement in SMEs

Significant software process improvement (SPI) of any kind requires a substantial investment of effort, time and money on part of organizations that try to follow it. SPI based on capability maturity model integration (CMMI) is no exception and adopting it is even more challenging for many small and medium enterprises (SMEs). However, it is becoming increasingly important for SMEs to get involved in SPI initiatives to gain imperative competitive advantage and survive in the software industry. In this paper, we explain how SMEs can adopt CMMI by first tailoring it to suit their requirements and then blending the cut-down version with Six Sigmas define, measure, analyze, improve, and control (DMAIC) methodology to reduce the time required to attain CMMI maturity level 2 and 3. Moreover, we recommend standard templates and Six Sigma tools to maintain and control CMMI artifacts for different process areas (PAs). We show the effectiveness of our model by applying it on two PAs. Our proposed model is intended to accelerate CMMI adoption by SMEs and to help them compete better in the international market.