ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Fainekos, Georgios
Description
In this thesis we deal with the problem of temporal logic robustness estimation. We present a dynamic programming algorithm for the robust estimation problem of Metric Temporal Logic (MTL) formulas regarding a finite trace of time stated sequence. This algorithm not only tests if the MTL specification is satisfied by the given input which is a finite system trajectory, but also quantifies to what extend does the sequence satisfies or violates the MTL specification. The implementation of the algorithm is the DP-TALIRO toolbox for MATLAB. Currently it is used as the temporal logic robust computing engine of S-TALIRO which is a tool for MATLAB searching for trajectories of minimal robustness in Simulink/ Stateflow. DP-TALIRO is expected to have near linear running time and constant memory requirement depending on the structure of the MTL formula. DP-TALIRO toolbox also integrates new features not supported in its ancestor FW-TALIRO such as parameter replacement, most related iteration and most related predicate. A derivative of DP-TALIRO which is DP-T-TALIRO is also addressed in this thesis which applies dynamic programming algorithm for time robustness computation. We test the running time of DP-TALIRO and compare it with FW-TALIRO. Finally, we present an application where DP-TALIRO is used as the robustness computation core of S-TALIRO for a parameter estimation problem.
ContributorsYang, Hengyi (Author) / Fainekos, Georgios (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2013
Description
Dynamic software update (DSU) enables a program to update while it is running. DSU aims to minimize the loss due to program downtime for updates. Usually DSU is done in three steps: suspending the execution of an old program, mapping the execution state from the old program to a new one, and resuming execution of the new program with the mapped state. The semantic correctness of DSU depends largely on the state mapping which is mostly composed by developers manually nowadays. However, the manual construction of a state mapping does not necessarily ensure sound and dependable state mapping. This dissertation presents a methodology to assist developers by automating the construction of a partial state mapping with a guarantee of correctness.
This dissertation includes a detailed study of DSU correctness and automatic state mapping for server programs with an established user base. At first, the dissertation presents the formal treatment of DSU correctness and the state mapping problem. Then the dissertation presents an argument that for programs with an established user base, dynamic updates must be backward compatible. The dissertation next presents a general definition of backward compatibility that specifies the allowed changes in program interaction between an old version and a new version and identified patterns of code evolution that results in backward compatible behavior. Thereafter the dissertation presents formal definitions of these patterns together with proof that any changes to programs in these patterns will result in backward compatible update. To show the applicability of the results, the dissertation presents SitBack, a program analysis tool that has an old version program and a new one as input and computes a partial state mapping under the assumption that the new version is backward compatible with the old version.
SitBack does not handle all kinds of changes and it reports to the user in incomplete part of a state mapping. The dissertation presents a detailed evaluation of SitBack which shows that the methodology of automatic state mapping is promising in deal with real world program updates. For example, SitBack produces state mappings for 17-75% of the changed functions. Furthermore, SitBack generates automatic state mapping that leads to successful DSU. In conclusion, the study presented in this dissertation does assist developers in developing state mappings for DSU by automating the construction of state mappings with a correctness guarantee, which helps the adoption of DSU ultimately.
This dissertation includes a detailed study of DSU correctness and automatic state mapping for server programs with an established user base. At first, the dissertation presents the formal treatment of DSU correctness and the state mapping problem. Then the dissertation presents an argument that for programs with an established user base, dynamic updates must be backward compatible. The dissertation next presents a general definition of backward compatibility that specifies the allowed changes in program interaction between an old version and a new version and identified patterns of code evolution that results in backward compatible behavior. Thereafter the dissertation presents formal definitions of these patterns together with proof that any changes to programs in these patterns will result in backward compatible update. To show the applicability of the results, the dissertation presents SitBack, a program analysis tool that has an old version program and a new one as input and computes a partial state mapping under the assumption that the new version is backward compatible with the old version.
SitBack does not handle all kinds of changes and it reports to the user in incomplete part of a state mapping. The dissertation presents a detailed evaluation of SitBack which shows that the methodology of automatic state mapping is promising in deal with real world program updates. For example, SitBack produces state mappings for 17-75% of the changed functions. Furthermore, SitBack generates automatic state mapping that leads to successful DSU. In conclusion, the study presented in this dissertation does assist developers in developing state mappings for DSU by automating the construction of state mappings with a correctness guarantee, which helps the adoption of DSU ultimately.
ContributorsShen, Jun (Author) / Bazzi, Rida A (Thesis advisor) / Fainekos, Georgios (Committee member) / Neamtiu, Iulian (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2015
Description
Most embedded applications are constructed with multiple threads to handle concurrent events. For optimization and debugging of the programs, dynamic program analysis is widely used to collect execution information while the program is running. Unfortunately, the non-deterministic behavior of multithreaded embedded software makes the dynamic analysis difficult. In addition, instrumentation overhead for gathering execution information may change the execution of a program, and lead to distorted analysis results, i.e., probe effect. This thesis presents a framework that tackles the non-determinism and probe effect incurred in dynamic analysis of embedded software. The thesis largely consists of three parts. First of all, we discusses a deterministic replay framework to provide reproducible execution. Once a program execution is recorded, software instrumentation can be safely applied during replay without probe effect. Second, a discussion of probe effect is presented and a simulation-based analysis is proposed to detect execution changes of a program caused by instrumentation overhead. The simulation-based analysis examines if the recording instrumentation changes the original program execution. Lastly, the thesis discusses data race detection algorithms that help to remove data races for correctness of the replay and the simulation-based analysis. The focus is to make the detection efficient for C/C++ programs, and to increase scalability of the detection on multi-core machines.
ContributorsSong, Young Wn (Author) / Lee, Yann-Hang (Thesis advisor) / Shrivastava, Aviral (Committee member) / Fainekos, Georgios (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2015
Description
Cyber-Physical Systems (CPS) are being used in many safety-critical applications. Due to the important role in virtually every aspect of human life, it is crucial to make sure that a CPS works properly before its deployment. However, formal verification of CPS is a computationally hard problem. Therefore, lightweight verification methods such as testing and monitoring of the CPS are considered in the industry. The formal representation of the CPS requirements is a challenging task. In addition, checking the system outputs with respect to requirements is a computationally complex problem. In this dissertation, these problems for the verification of CPS are addressed. The first method provides a formal requirement analysis framework which can find logical issues in the requirements and help engineers to correct the requirements. Also, a method is provided to detect tests which vacuously satisfy the requirement because of the requirement structure. This method is used to improve the test generation framework for CPS. Finally, two runtime verification algorithms are developed for off-line/on-line monitoring with respect to real-time requirements. These monitoring algorithms are computationally efficient, and they can be used in practical applications for monitoring CPS with low runtime overhead.
ContributorsDokhanchi, Adel (Author) / Fainekos, Georgios (Thesis advisor) / Lee, Yann-Hang (Committee member) / Sarjoughian, Hessam S. (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2017
Description
Designers employ a variety of modeling theories and methodologies to create functional models of discrete network systems. These dynamical models are evaluated using verification and validation techniques throughout incremental design stages. Models created for these systems should directly represent their growing complexity with respect to composition and heterogeneity. Similar to software engineering practices, incremental model design is required for complex system design. As a result, models at early increments are significantly simpler relative to real systems. While experimenting (verification or validation) on models at early increments are computationally less demanding, the results of these experiments are less trustworthy and less rewarding. At any increment of design, a set of tools and technique are required for controlling the complexity of models and experimentation.
A complex system such as Network-on-Chip (NoC) may benefit from incremental design stages. Current design methods for NoC rely on multiple models developed using various modeling frameworks. It is useful to develop frameworks that can formalize the relationships among these models. Fine-grain models are derived using their coarse-grain counterparts. Moreover, validation and verification capability at various design stages enabled through disciplined model conversion is very beneficial.
In this research, Multiresolution Modeling (MRM) is used for system level design of NoC. MRM aids in creating a family of models at different levels of scale and complexity with well-formed relationships. In addition, a variant of the Discrete Event System Specification (DEVS) formalism is proposed which supports model checking. Hierarchical models of Network-on-Chip components may be created at different resolutions while each model can be validated using discrete-event simulation and verified via state exploration. System property expressions are defined in the DEVS language and developed as Transducers which can be applied seamlessly for model checking and simulation purposes.
Multiresolution Modeling with verification and validation capabilities of this framework complement one another. MRM manages the scale and complexity of models which in turn can reduces V&V time and effort and conversely the V&V helps ensure correctness of models at multiple resolutions. This framework is realized through extending the DEVS-Suite simulator and its applicability demonstrated for exemplar NoC models.
A complex system such as Network-on-Chip (NoC) may benefit from incremental design stages. Current design methods for NoC rely on multiple models developed using various modeling frameworks. It is useful to develop frameworks that can formalize the relationships among these models. Fine-grain models are derived using their coarse-grain counterparts. Moreover, validation and verification capability at various design stages enabled through disciplined model conversion is very beneficial.
In this research, Multiresolution Modeling (MRM) is used for system level design of NoC. MRM aids in creating a family of models at different levels of scale and complexity with well-formed relationships. In addition, a variant of the Discrete Event System Specification (DEVS) formalism is proposed which supports model checking. Hierarchical models of Network-on-Chip components may be created at different resolutions while each model can be validated using discrete-event simulation and verified via state exploration. System property expressions are defined in the DEVS language and developed as Transducers which can be applied seamlessly for model checking and simulation purposes.
Multiresolution Modeling with verification and validation capabilities of this framework complement one another. MRM manages the scale and complexity of models which in turn can reduces V&V time and effort and conversely the V&V helps ensure correctness of models at multiple resolutions. This framework is realized through extending the DEVS-Suite simulator and its applicability demonstrated for exemplar NoC models.
ContributorsGholami, Soroosh (Author) / Sarjoughian, Hessam S. (Thesis advisor) / Fainekos, Georgios (Committee member) / Ogras, Umit Y. (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2017
Description
Automated driving systems are in an intensive research and development stage, and the companies developing these systems are targeting to deploy them on public roads in a very near future. Guaranteeing safe operation of these systems is crucial as they are planned to carry passengers and share the road with other vehicles and pedestrians. Yet, there is no agreed-upon approach on how and in what detail those systems should be tested. Different organizations have different testing approaches, and one common approach is to combine simulation-based testing with real-world driving.
One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. Besides safety, there are other expectations from automated vehicles such as comfortable driving and minimal fuel consumption. All safety and functional expectations from an automated driving system should be captured with a set of system requirements. It is challenging to create requirements that are unambiguous and usable for the design, testing, and evaluation of automated driving systems. Another challenge is to define useful metrics for assessing the testing quality because in general, it is impossible to test every possible scenario.
The goal of this dissertation is to formalize the theory for testing automated vehicles. Various methods for automatic test generation for automated-driving systems in simulation environments are presented and compared. The contributions presented in this dissertation include (i) new metrics that can be used to discover the boundary cases between safe and unsafe driving conditions, (ii) a new approach that combines combinatorial testing and optimization-guided test generation methods, (iii) approaches that utilize global optimization methods and random exploration to generate critical vehicle and pedestrian trajectories for testing purposes, (iv) a publicly-available simulation-based automated vehicle testing framework that enables application of the existing testing approaches in the literature, including the new approaches presented in this dissertation.
One of the expectations from fully-automated vehicles is never to cause an accident. However, an automated vehicle may not be able to avoid all collisions, e.g., the collisions caused by other road occupants. Hence, it is important for the system designers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. Besides safety, there are other expectations from automated vehicles such as comfortable driving and minimal fuel consumption. All safety and functional expectations from an automated driving system should be captured with a set of system requirements. It is challenging to create requirements that are unambiguous and usable for the design, testing, and evaluation of automated driving systems. Another challenge is to define useful metrics for assessing the testing quality because in general, it is impossible to test every possible scenario.
The goal of this dissertation is to formalize the theory for testing automated vehicles. Various methods for automatic test generation for automated-driving systems in simulation environments are presented and compared. The contributions presented in this dissertation include (i) new metrics that can be used to discover the boundary cases between safe and unsafe driving conditions, (ii) a new approach that combines combinatorial testing and optimization-guided test generation methods, (iii) approaches that utilize global optimization methods and random exploration to generate critical vehicle and pedestrian trajectories for testing purposes, (iv) a publicly-available simulation-based automated vehicle testing framework that enables application of the existing testing approaches in the literature, including the new approaches presented in this dissertation.
ContributorsTuncali, Cumhur Erkan (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Heni (Committee member) / Kapinski, James (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2019
Description
User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven approach to predicting power and performance. The approach makes no assumptions on the distributions of the underlying sources of uncertainty and is capable of predicting power and performance with over 93% accuracy.
Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.
Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.
ContributorsGaudette, Benjamin David (Author) / Vrudhula, Sarma (Thesis advisor) / Wu, Carole-Jean (Thesis advisor) / Fainekos, Georgios (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2017
Description
There has been exciting progress in the area of Unmanned Aerial Vehicles (UAV) in the last decade, especially for quadrotors due to their nature of easy manipulation and simple structure. A lot of research has been done on achieving autonomous and robust control for quadrotors. Recently researchers have been utilizing linear temporal logic as mission specification language for robot motion planning due to its expressiveness and scalability. Several algorithms have been proposed to achieve autonomous temporal logic planning. Also, several frameworks are designed to compose those discrete planners and continuous controllers to make sure the actual trajectory also satisfies the mission specification. However, most of these works use first-order kinematic models which are not accurate when quadrotors fly at high speed and cannot fully utilize the potential of quadrotors.
This thesis work describes a new design for a hierarchical hybrid controller that is based on a dynamic model and seeks to achieve better performance in terms of speed and accuracy compared with some previous works. Furthermore, the proposed hierarchical controller is making progress towards guaranteed satisfaction of mission specification expressed in Linear Temporal Logic for dynamic systems. An event-driven receding horizon planner is also utilized that aims at distributed and decentralized planning for large-scale navigation scenarios. The benefits of this approach will be demonstrated using simulations results.
This thesis work describes a new design for a hierarchical hybrid controller that is based on a dynamic model and seeks to achieve better performance in terms of speed and accuracy compared with some previous works. Furthermore, the proposed hierarchical controller is making progress towards guaranteed satisfaction of mission specification expressed in Linear Temporal Logic for dynamic systems. An event-driven receding horizon planner is also utilized that aims at distributed and decentralized planning for large-scale navigation scenarios. The benefits of this approach will be demonstrated using simulations results.
ContributorsZhang, Xiaotong (Author) / Fainekos, Georgios (Thesis advisor) / Ben Amor, Heni (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2016
Description
Autonomous Vehicles (AV) are inevitable entities in future mobility systems thatdemand safety and adaptability as two critical factors in replacing/assisting human
drivers. Safety arises in defining, standardizing, quantifying, and monitoring requirements
for all autonomous components. Adaptability, on the other hand, involves
efficient handling of uncertainty and inconsistencies in models and data. First, I address
safety by presenting a search-based test-case generation framework that can be
used in training and testing deep-learning components of AV. Next, to address adaptability,
I propose a framework based on multi-valued linear temporal logic syntax and
semantics that allows autonomous agents to perform model-checking on systems with
uncertainties. The search-based test-case generation framework provides safety assurance
guarantees through formalizing and monitoring Responsibility Sensitive Safety
(RSS) rules. I use the RSS rules in signal temporal logic as qualification specifications
for monitoring and screening the quality of generated test-drive scenarios. Furthermore,
to extend the existing temporal-based formal languages’ expressivity, I propose
a new spatio-temporal perception logic that enables formalizing qualification specifications
for perception systems. All-in-one, my test-generation framework can be
used for reasoning about the quality of perception, prediction, and decision-making
components in AV. Finally, my efforts resulted in publicly available software. One
is an offline monitoring algorithm based on the proposed logic to reason about the
quality of perception systems. The other is an optimal planner (model checker) that
accepts mission specifications and model descriptions in the form of multi-valued logic
and multi-valued sets, respectively. My monitoring framework is distributed with the
publicly available S-TaLiRo and Sim-ATAV tools.
ContributorsHekmatnejad, Mohammad (Author) / Fainekos, Georgios (Thesis advisor) / Deshmukh, Jyotirmoy V (Committee member) / Karam, Lina (Committee member) / Pedrielli, Giulia (Committee member) / Shrivastava, Aviral (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021