Design of Interactive Feature Space Construction Protocol

Machine learning deals with designing systems that learn from data i.e. automatically improve with experience. Systems gain experience by detecting patterns or regularities and using them for making predictions. These predictions are based on the properties that the system learns from the data. Thus when we say a machine learns, it means it has changed in a way that allows it to perform more efficiently than before. Machine learning is emerging as an important technology for solving a number of applications involving natural language processing applications, medical diagnosis, game playing or financial applications. Wide variety of machine learning approaches have been developed and used for a number of applications. We first review the work done in the field of machine learning and analyze various concepts about machine learning that are applicable to the work presented in this thesis. Next we examine active machine learning for pipelining of an important natural language application i.e. information extraction, in which the task of prediction is carried out in different stages and the output of each stage serves as an input to the next stage. A number of machine learning algorithms have been developed for different applications. However no single machine learning algorithm can be used appropriately for all learning problems. It is not possible to create a general learner for all problems because there are varied types of real world datasets that cannot be handled by a single learner. For this purpose an evaluation of the machine learning algorithms is needed. We present an experiment for the evaluation of various state-of-the-art machine learning algorithms using an interactive machine learning tool called WEKA (Waikato Environment for Knowledge Analysis). Evaluation is carried out with the purpose of finding an optimal solution for a real world learning problemcredit approval used in banks. It is a classification problem. Finally, we present an approach of combining various learners with the aim of increasing their efficiency. We present two experiments that evaluate the machine learning algorithms for efficiency and compare their performance with the new combined approach, for the same classification problem. Later we show the effects of feature selection on the efficiency of our combined approach as well as on other machine learning techniques. The aim of this work is to analyze the techniques that increase the efficiency of the learners

Framework for Human Computer Interaction for Learning Dialogue Strategies using Controlled Natural Language in Information Systems

Spoken Language systems are going to have a tremendous impact in all the real world applications, be it healthcare enquiry, public transportation system or airline booking system maintaining the language ethnicity for interaction among users across the globe. These system have the capability of interacting with the user in di erent languages that the system supports. Normally when a person interacts with another person there are many non-verbal clues which guide the dialogue and all the utterances have a contextual relationship, which manage the dialogue as its mixed by the two speakers. Human Computer Interaction has a wide impact on the design of the applications and has become one of the emerging interest area of the researchers. All of us are witness to an explosive electronic revolution where lots of gadgets and gizmo's have surrounded us, advanced not only in power, design, applications but the ease of access or what we call user friendly interfaces are designed that we can easily use and control all the functionality of the devices. Since speech is one of the most intuitive form of interaction that humans use. It provides potential bene ts such as handfree access to machines, ergonomics and greater e ciency of interaction. Yet, speech-based interfaces design has been an expert job for a long time. Lot of research has been done in building real spoken Dialogue Systems which can interact with humans using voice interactions and help in performing various tasks as are done by humans. Last two decades have seen utmost advanced research in the automatic speech recognition, dialogue management, text to speech synthesis and Natural Language Processing for various applications which have shown positive results. This dissertation proposes to apply machine learning (ML) techniques to the problem of optimizing the dialogue management strategy selection in the Spoken Dialogue system prototype design. Although automatic speech recognition and system initiated dialogues where the system expects an answer in the form of `yes' or `no' have already been applied to Spoken Dialogue Systems( SDS), no real attempt to use those techniques in order to design a new system from scratch has been made. In this dissertation, we propose some novel ideas in order to achieve the goal of easing the design of Spoken Dialogue Systems and allow novices to have access to voice technologies. A framework for simulating and evaluating dialogues and learning optimal dialogue strategies in a controlled Natural Language is proposed. The simulation process is based on a probabilistic description of a dialogue and on the stochastic modelling of both arti cial NLP modules composing a SDS and the user. This probabilistic model is based on a set of parameters that can be tuned from the prior knowledge from the discourse or learned from data. The evaluation is part of the simulation process and is based on objective measures provided by each module. Finally, the simulation environment is connected to a learning agent using the supplied evaluation metrics as an objective function in order to generate an optimal behaviour for the SDS

Modeling of Security Measurement (Metrics) in an Information System

Security metrics and measurement is a sub-field of broader information security field. This field is not new but it got very least and sporadic attention as a result of which it is still in its early stages. The measurement and evaluation of security now became a long standing challenge to the research community. Much of the focus remained towards devising and the application of new and updated protection mechanisms. Measurements in general act as a driving force in decision making. As stated by Lord Kelvin “if you cannot measure it then you cannot improve it”. This principle is also applicable to security measurement of information systems. Even if the necessary and required protection mechanisms are in place still the level of security remains unknown, which limits the decision making capabilities to improve the security of a system. With the increasing reliance on these information systems in general and software systems in particular security measurement has become the most pressing requirement in order to promote and develop the security critical systems in the current networked environment. The resultant indicators of security measurement preferably the quantative indicators act as a basis for the decision making to enhance the security of overall system. The information systems are comprised of various components such as people, hardware, data, network and software. With the fast growing reliance on the software systems, the research reported in this thesis aims to provide a framework using mathematical modeling techniques for evaluation of security of the software systems at the architectural and design phase of the system lifecycle and the derived security metrics on a controlled scale from the proposed framework. The proposed security evaluation framework is independent of the programing language and the platform used in developing the system and also is applicable from small desktop application to large complex distributed software. The validation process of security metrics is the most challenging part of the security metrics field. In this thesis we have conducted the exploratory empirical evaluation on a running system to validate the derived security metrics and the measurement results. To make the task easy we have transformed the proposed security evaluation into algorithmic form which increased the applicability of the proposed framework without requiring any expert security knowledge. The motivation of the research is to provide the software development team with a tool to evaluate the level of security of each of the element of the system and the overall system at the early development stages of the system life cycle. In this regard three question “What is to be measured?”, “where (in the system life cycle) to measure?” and “how to measure?” have been answered in the thesis. Since the field of security metrics and measurements is still in the its early stages, the first part of the thesis investigates and analyzes the basic terminologies , taxonomies and major efforts made towards security metrics based on the literature survey. Answering the second question “Where (in the system life cycle) to measure security”, the second part of the thesis analyzes the secure software development processes (SSDPs) followed and identifies the key stages of the system’s life cycle where the evaluation of security is necessary. Answering the question 1 and 2, “What is to be measured “and “How to measure”, third part of the thesis presents a security evaluation framework aimed at the software architecture and design phase using mathematical modeling techniques. In the proposed framework, the component based architecture and design (CBAD) using UML 2.0 component modeling techniques has been adopted. Further in part 3 of the thesis present the empirical evaluation of the proposed framework to validate and analyze the applicability and feasibility of the proposed security metrics. Our effort is to get the focus of the software development community to focus on the security evaluation in the software development process in order to take the early decisions regarding the security of the overall system

Software Reliability Growth Models from the Perspective of Learning Effects and Change-Point.

Increased attention towards reliability of software systems has led to the thorough analysis of the process of reliability growth for prediction and assessment of software reliability in the testing or debugging phase. With many frameworks available in terms of the underlying probability distributions like Poisson process, Non-Homogeneous Poisson Process (NHPP), Weibull, etc, many researchers have developed models using the Non-Homogeneous Poisson Process (NHPP) analytical framework. The behavior of interest, usually, is S-shaped or exponential shaped. S-shaped behavior could relate more closely to the human learning. The need to develop different models stems from the fact that nature of the underlying environment, learning effect acquisition during testing, resource allocations, application and the failure data itself vary. There is no universal model that fits everywhere to be called an Oracle. Learning effects that stem from the experiences of the testing or debugging staff have been considered for the growth of reliability. Learning varies over time and this asserts need for conduct of more research for study of learning effects.Digital copy of ThesisUniversity of Kashmi

Evaluating Effectiveness of Software Testing Techniques with Emphasis on Enhancing Software Reliability

Software testing is one of the most widely known and essential field in software engineering. The purpose of software testing is not only to reveal defects and eliminate them but also to serve as a tool for verification, validation and certification. Defection detection and increasing reliability are the two main goals of software testing. For decades, researchers have been inventing new techniques to test software. However, no testing technique will ever be a solution for all types of software defects. At present, we have very limited information of software testing techniques effectiveness and efficiency. Therefore, while researchers should continue to develop new testing techniques, they also need to deeply understand the abilities and limitations of existing techniques. We need to know what types of defects a particular technique can be expected to find and at what cost. We have to check whether testing technique effectiveness and efficiency depends on program to which it is applied, subject who applies it, the number of faults in the program or the type of faults in the program. However it is not sufficient if testing techniques are only compared on fault detecting ability. They should also be evaluated to check which among them enhances reliability. The research in this thesis aims at evaluating software testing techniques in terms of effectiveness in detecting software defects, and the ability to increase the reliability of the software. The research in this thesis falls within empirical method research on the verification and validation process with a focus on software testing techniques evaluation. The work in this thesis links both research and practice and aims to continue building empirical knowledge in the field of software engineering. The first part of this thesis surveys and analyzes empirical studies on evaluation of testing techniques. Issues with the current evaluation of software testing techniques are identified. Building upon this, we present an evaluation framework (a set of guidelines) for experiments which evaluate the software testing techniques. In addition, we also proposed a uniform classification of software testing techniques and identified a set of factors which helps us in selecting an appropriate testing technique. The second part of the thesis presents an experiment which evaluates and com- pares three defect detection techniques to evaluate the effectiveness of the software testing techniques in terms of defect detection. Moreover, we also evaluated the efficiency of these techniques. The dependence of the effectiveness and efficiency on the various parameters like programs, subjects and defects is also investigated in the experiment. The third and final part of this thesis presents an experiment which evaluates and compares three defect detection techniques for reliability using a novel method. The efficiency of these techniques is also evaluated. Our effort is to provide evidence that will help testing and research community to understand the effectiveness and efficiency of software testing techniques in terms of defect detection and reliability and their dependence on various factors. The ultimate goal of our work is to move software engineering from a craft towards an engineering discipline