With advances in hardware miniaturization and wireless communication technologies even small portable wireless devices have much communication bandwidth and computing power. These devices include smartphones, tablet computers, and personal digital assistants. Users of these devices expect to run software applications that they usually have on their desktop computers as well as the new applications that are being developed for mobile devices. Web browsing, social networking, gaming, online multimedia playing, global positioning system based navigation, and accessing emails are examples of a few popular applications. Mobile versions of thousands of desktop applications are already available in mobile application markets, and consequently, the expected operational time of smartphones is rising rapidly. At the same time, the complexity of these applications is growing in terms of computation and communication needs, and there is a growing demand for energy in smartphones. However, unlike the exponential growth in computing and communication technologies, in terms of speed and packaging density, battery technology has not kept pace with the rapidly growing energy demand of these devices. Therefore, designers are faced with the need to enhance the battery life of smartphones. Knowledge of how energy is used and lost in the system components of the devices is vital to this end. With this view, we focus on modeling and evaluating the energy performance of smartphones in this thesis. We also propose techniques for enhancing the energy efficiency and functionality of smartphones. The detailed contributions of the thesis are as follows: (i) we present a nite state machine based model to estimate the energy cost of an application running on a smartphone, and provide practical approaches to extract model parameters; (ii) the concept of energy cost pro le is introduced to assess the impact of design decisions on energy cost at an early stage of software design; (iii) a generic architecture is proposed and implemented for enhancing the capabilities of smartphones by sharing resources; (iv) we have analyzed the Internet tra c of smartphones to observe the energy saving potentials, and have studied the implications on the existing energy saving techniques; and nally, (v) we have provided a methodology to select user level test cases for performing energy cost evaluation of applications. All of our concepts and proposed methodology have been validated with extensive measurements on a real test bench. Our work contributes to both theoretical understanding of energy e ciency of software applications and practical methodologies for evaluating energy e ciency. In summary, the results of this work can be used by application developers to make implementation level decisions that affect the energy efficiency of software applications on smartphones. In addition, this work leads to the design and implementation of energy e cient smartphones.

With advances in hardware miniaturization and wireless communication technologies even small portable wireless devices have much communication bandwidth and computing power. These devices include smartphones, tablet computers, and personal digital assistants. Users of these devices expect to run software applications that they usually have on their desktop computers as well as the new applications that are being developed for mobile devices. Web browsing, social networking, gaming, online multimedia playing, global positioning system based navigation, and accessing emails are examples of a few popular applications. Mobile versions of thousands of desktop applications are already available in mobile application markets, and consequently, the expected operational time of smartphones is rising rapidly. At the same time, the complexity of these applications is growing in terms of computation and communication needs, and there is a growing demand for energy in smartphones. However, unlike the exponential growth in computing and communication technologies, in terms of speed and packaging density, battery technology has not kept pace with the rapidly growing energy demand of these devices. Therefore, designers are faced with the need to enhance the battery life of smartphones. Knowledge of how energy is used and lost in the system components of the devices is vital to this end. With this view, we focus on modeling and evaluating the energy performance of smartphones in this thesis. We also propose techniques for enhancing the energy efficiency and functionality of smartphones. The detailed contributions of the thesis are as follows: (i) we present a nite state machine based model to estimate the energy cost of an application running on a smartphone, and provide practical approaches to extract model parameters; (ii) the concept of energy cost pro le is introduced to assess the impact of design decisions on energy cost at an early stage of software design; (iii) a generic architecture is proposed and implemented for enhancing the capabilities of smartphones by sharing resources; (iv) we have analyzed the Internet tra c of smartphones to observe the energy saving potentials, and have studied the implications on the existing energy saving techniques; and nally, (v) we have provided a methodology to select user level test cases for performing energy cost evaluation of applications. All of our concepts and proposed methodology have been validated with extensive measurements on a real test bench. Our work contributes to both theoretical understanding of energy e ciency of software applications and practical methodologies for evaluating energy e ciency. In summary, the results of this work can be used by application developers to make implementation level decisions that affect the energy efficiency of software applications on smartphones. In addition, this work leads to the design and implementation of energy e cient smartphones.

This thesis is focused on ways of defining the requirements for performance testing software, both in practice (based on survey) as well as in theory. The aim of this thesis is to analyze ways of defining performance requirements, which are actually used on IT projects. In order to achieve this goal it is necessary to define concepts of performance testing, implement and evaluate the survey of the using ways of defining performance requirements in practice and then analyze ways of defining performance requirements in terms of their applicability to different types of IT projects. The contribution of this thesis is the comprehensive introduction performance testing software issues and mainly insight into ways of defining performance requirements used in practice and problems associated with them, through the survey, which was implemented and evaluated by myself. The conclusions resulting from this survey summarize which ways of defining performance requirements are applied to specific types of IT projects, which of these ways worked and which problems in certain cases that occur in practice. The thesis is divided into theoretical and practical part. The theoretical part explains the basic concepts associated with performance testing software. In this part, there is also described the methodology of defining performance requirements according to Tom Gilb. The practical part is focused on the realization and evaluation of survey of the using ways of defining performance requirements in practice and on the analysis ways of defining performance requirements with respect to certain types of projects.

Indiana University-Purdue University Indianapolis (IUPUI) / Evolutionary testing (ET) techniques (e.g., mutation, crossover, and natural selection) have been applied successfully to many areas of software engineering, such as error/fault identification, data mining, and software cost estimation. Previous research has also applied ET techniques to performance testing. Its application to performance testing, however, only goes as far as finding the best and worst case, execution times. Although such performance testing is beneficial, it provides little insight into performance characteristics of complex functions with multiple branches. This thesis therefore provides two contributions towards performance testing of software systems. First, this thesis demonstrates how ET and genetic algorithms (GAs), which are search heuristic mechanisms for solving optimization problems using mutation, crossover, and natural selection, can be combined with a constraint solver to target specific paths in the software. Secondly, this thesis demonstrates how such an approach can identify local minima and maxima execution times, which can provide a more detailed characterization of software performance. The results from applying our approach to example software applications show that it is able to characterize different execution paths in relatively short amounts of time. This thesis also examines a modified exhaustive approach which can be plugged in when the constraint solver cannot properly provide the information needed to target specific paths.