AUTOMATED SOFTWARE TESTING PROGRAM BASED ON THE TABU SEARCH

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CHAPTER 2 BACKGROUND

This chapter presents the basics that are needed to understand the research in the later chapters. The cognitive radio that is to be tested will be described as well as the genetic algorithm used to choose the signal and the USRP individually. A brief background on software testing will be presented with different approaches that have been researched and compared. Lastly, a description of how the tabu search works and comparisons to other approaches will be examined.

COGNITIVE RADIOS

Cognitive radios are a very interesting research area because of their ability to efficiently use the radio frequency (RF) spectrum and optimize the transmission of a signal dependent on the users need, with little or no intervention by an operator. This has many advantages over current radio systems, from the ease of use to the ability to transmit a signal in “white spaces” where current systems cannot transmit. Due to these advantages, cognitive radios can have widespread appeal among users with many varied goals.The term “cognitive radio” and the basic understanding of the subject was developed by Joseph Mitola who published his ideas in multiple publications [4][5][6][7], and eventually in a doctorial thesis [8]. His work has sparked a variety of research into the area of cognitive radios. Currently, there is no standard definition for cognitive radio,and because of this, there are a wide variety of definitions. Some definitions of a cognitive radio may just be another person’s definition of an adaptive radio. The work done on this project defines a cognitive radio as a transceiver that is aware of the following [1]:
• The RF environment
• Its own power, frequency and waveform capabilities
• Its users requirements and operating abilities
• The regulations, etiquette, and protocols that governs its operation This definition of a cognitive radio is very similar to how Mitola descried a cognitive radio in [4].Here he called it a goal-driven framework where the radio autonomously observes the radio environment, infers context, assesses alternatives, generates plans,supervises services, and learns from its mistakes. He continued by pointing out that this observe-think-act cycle is radically different from today’s handsets that either blast out on the frequency set by the user, or blindly take instructions from the network. Cognitive radio technology thus empowers radios to observe more flexible radio etiquettes than was possible in the past.Cognitive radios operate through the use of “knobs” and “meters” inside a software defined radio (SDR) platform. The “meters” represent the ability for the radio to receive and measure the stimulus from the outside world. Based upon what the“meters” read, and the goals of the radio, the cognitive engine will then turn the “knobs” to obtain the optimal signal under those circumstances. This is one of the advantages of a cognitive radio, the ability to modify itself and deal with unexpected situations. Once the signal has been decided upon in the cognitive engine, it is sent to the SDR, where the radio is altered to the desired settings and sends, or prepares to receive, any possible signal.

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SOFTWARE DEFINED RADIO (SDR)

Software defined radios are an integral part of a cognitive radio. SDRs allow a single radio to produce and receive a variety of signals with minimal hardware use,allowing a cognitive radio to automatically reconfigure a signal. The advantages of SDRs have resulted in widespread use by the military and for cell phone service, and, in the long run, its proponents expect it to become the dominant technology in radio communications.An SDR is a multi-band radio capable of supporting a wide range of air interfaces and protocols. It is, as the name would suggest, controlled by a software program that is used to modulate and demodulate a signal. Ideally, all functions of a radio would be processed by a general purpose processor, which is currently impractical for some signals for one reason or another (i.e. power consumption). Currently those signals are processed through application specific integrated circuits (ASICs), which are combined with field programmable gate arrays (FPGAs), digital signal processing (DSP) and general purpose processors to create the processing power of an SDR. As processor technology increases,the likelihood that all signals can be processed on general purpose processors is increasing [4]. This would make SDRs very feature-rich radios that are relatively inexpensive. In a cognitive radio, the SDR is used to produce or receive the signal. The specification of the signal comes from an interface the SDR has with the cognitive engine.

COGNITIVE ENGINE

The cognitive engine is the brain of the cognitive radio. It observes the radio environment, plans on what actions to take, learns from past actions, and decides on the best action to take. It is in control of all aspects of the radio, from specifications to etiquette. It is designed to automatically produce the best signal dependent on the user’s desires, and is sometimes built to emulate the actions an operator would take if he were in control of the radio. There are many possible algorithms to implement a cognitive engine, partly due to the different definitions of cognitive radio, but also partly due to continuing research into the field. The cognitive engine in this project is based on biologically inspired genetic algorithms. How genetics algorithms work will be presented later in this chapter.

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LIST OF CONTENTS
LIST OF FIGURES
LIST OF TABLES
CHAPTER 1 INTRODUCTION
1.1 Motivation
1.2 Approach and Contributions
1.3 Thesis Outline
CHAPTER 2 BACKGROUND
2.1 Cognitive Radios
2.1.1 Software Defined Radios (SDR)
2.1.2 Cognitive Engine
2.1.3 Genetic Algorithm
2.2 Software Testing
2.2.1 Static Testing
2.2.2 Dynamic Testing
2.2.2.1 Coverage Metric
2.2.2.2 Data Generation
2.2.2.3 Error Evaluation
2.3 Tabu Search
2.3.1 Search Procedure
2.3.2 Memory Structures
2.3.2.1 Short-Term Memory
2.3.2.2 Long-Term Memory
CHAPTER 3 AUTOMATED SOFTWARE TESTING PROGRAM BASED ON THE TABU SEARCH
3.1 Coverage Metric
3.1.1 Branch Coverage
3.1.2 Fitness Function
3.2 Data Generation
3.2.1 Initialization
3.2.2 Neighborhood Selection Process
3.2.3 Execution of Code
3.2.4 Search Space
3.2.5 Rules
3.2.6 Learning
3.2.7 Treatment of Unreached Branches
3.3 Error Analysis
3.4 Results
CHAPTER 4 BUILT-IN SELF-TEST FOR COGNITIVE RADIO
4.1 Policy Mask
4.1.1 UWB Mask for Communication Systems
4.1.2 Legality Focus
4.1.3 Quality Focus
4.1.4 Signal Modeling
4.1.5 Genetic Algorithm
4.1.6 Implementation
4.1.7 Results
4.2 API Checker
4.2.1 Radio API
4.2.2 Checker
4.2.3 Summary
CHAPTER 5 CONLUSIONS AND FUTURE WORK
5.1 Conclusions
5.2 Future Work
REFERENCES
APPENDIX A TRIANGLE PROGRAM
APPENDIX B COGNITIVE ENGINE MODULE TESTING OVERVIEW
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