Test generation for high coverage with abstraction refinement and coarsening (ARC)

Testing is the main approach used in the software industry to expose failures. Producing thorough test suites is an expensive and error prone task that can greatly benefit from automation. Two challenging problems in test automation are generating test input and evaluating the adequacy of test suites: the first amounts to producing a set of test cases that accurately represent the software behavior, the second requires defining appropriate metrics to evaluate the thoroughness of the testing activities. Structural testing addresses these problems by measuring the amount of code elements that are executed by a test suite. The code elements that are not covered by any execution are natural candidates for generating further test cases, and the measured coverage rate can be used to estimate the thoroughness of the test suite. Several empirical studies show that test suites achieving high coverage rates exhibit a high failure detection ability. However, producing highly covering test suites automatically is hard as certain code elements are executed only under complex conditions while other might be not reachable at all. In this thesis we propose Abstraction Refinement and Coarsening (ARC), a goal oriented technique that combines static and dynamic software analysis to automatically generate test suites with high code coverage. At the core of our approach there is an abstract program model that enables the synergistic application of the different analysis components. In ARC we integrate Dynamic Symbolic Execution (DSE) and abstraction refinement to precisely direct test generation towards the coverage goals and detect infeasible elements. ARC includes a novel coarsening algorithm for improved scalability. We implemented ARC-B, a prototype tool that analyses C programs and produces test suites that achieve high branch coverage. Our experiments show that the approach effectively exploits the synergy between symbolic testing and reachability analysis outperforming state of the art test generation approaches. We evaluated ARC-B on industry relevant software, and exposed previously unknown failures in a safety-critical software component

MaDe4IC: an abstract method for managing model dependencies in inter-organizational cooperations

Inter-organizational cooperations are complex in terms of coordination, agreements, and value creation for involved partners. When managing complex cooperations, it is vital to maintain models describing them. Changing one model to regain consistency with the running system might result in new inconsistencies. As a consequence, this maintenance phase grows in complexity with increasing number of models. In this context, challenges are to ensure consistency at design time and to monitor the system at runtime, i.e., at design time, consistency between different models describing the cooperation needs to be ensured. At runtime, behavior of the software system needs to be compared with its underlying models. In this paper, we propose a structured and model-independent method that supports ensuring and maintaining consistency between running system and underlying models for inter-organizational cooperations

Performance Portable Solid Mechanics via Matrix-Free pp-Multigrid

Finite element analysis of solid mechanics is a foundational tool of modern engineering, with low-order finite element methods and assembled sparse matrices representing the industry standard for implicit analysis. We use performance models and numerical experiments to demonstrate that high-order methods greatly reduce the costs to reach engineering tolerances while enabling effective use of GPUs. We demonstrate the reliability, efficiency, and scalability of matrix-free $p$-multigrid methods with algebraic multigrid coarse solvers through large deformation hyperelastic simulations of multiscale structures. We investigate accuracy, cost, and execution time on multi-node CPU and GPU systems for moderate to large models using AMD MI250X (OLCF Crusher), NVIDIA A100 (NERSC Perlmutter), and V100 (LLNL Lassen and OLCF Summit), resulting in order of magnitude efficiency improvements over a broad range of model properties and scales. We discuss efficient matrix-free representation of Jacobians and demonstrate how automatic differentiation enables rapid development of nonlinear material models without impacting debuggability and workflows targeting GPUs

Constructive Formal Control Synthesis through Abstraction and Decomposition

Control synthesis is the problem of automatically constructing a control strategy that induces a system to exhibit a declared behavior. Synthesis algorithms vary widely across different classes of system dynamics and specifications.While continuous optimization has traditionally been used to construct stabilizing controllers for physical systems modeled with differential equations, temporal logic synthesis for finite state machines heavily leverages discrete algorithms and data structures.Hybrid systems are a class of systems that exhibit both continuous and discrete behaviors, which are necessary to capture phenomena such as impacts for legged robots and congestion shockwaves in freeways. Tractable control synthesis remains elusive because hybrid systems violate many of the fundamental topological assumptions made by prior algorithms for purely continuous or discrete systems.This thesis exploits compositionality and system structure to provide a suite of algorithmic and theoretical techniques to tackle acute computational bottlenecks in hybrid control synthesis.The first half of this thesis provides a framework for engineers to model control systems and construct algorithmic pipelines for control synthesis.By explicitly capturing system structure, this framework gives users the flexibility to rapidly iterate over and leverage a library of optimizations for control synthesis.We demonstrate this framework in the context of abstraction-based control, a synthesis workflow that translates continuous systems into finite state machines by throwing away high precision information. Different optimization techniques such as multi-scale grids, lazy abstraction, and decomposed synthesis, can all be expressed as modifications to a computational pipeline. We demonstrate computational gains while synthesizing safe motion primitives for numerous robotic examples.The second half addresses distributed control synthesis where multiple controllers act as agents that seek to jointly satisfy a specification and are restricted by some communication topology. We introduce parametric assume-guarantee contracts as a formalism to derive guarantees about the closed loop behavior of a collection of interacting components. Dynamic contracts allow contract parameters to change at runtime and enable coordination of multiple interacting sub-systems.These results are demonstrated in the context of a freeway ramp meter and an adjacent arterial network

Software Analytics for Improving Program Comprehension

Title from PDF of title page viewed June 28, 2021Dissertation advisor: Yugyung LeeVitaIncludes bibliographical references (pages 122-143)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2021Program comprehension is an essential part of software development and maintenance. Traditional methods of program comprehension, such as reviewing the codebase and documentation, are still challenging for understanding the software's overall structure and implementation. In recent years, software static analysis studies have emerged to facilitate program comprehensions, such as call graphs, which represent the system’s structure and its implementation as a directed graph. Furthermore, some studies focused on semantic enrichment of the software system problems using systematic learning analytics, including machine learning and NLP. While call graphs can enhance the program comprehension process, they still face three main challenges: (1) complex call graphs can become very difficult to understand making call graphs much harder to visualize and interpret by a developer and thus increases the overhead in program comprehension; (2) they are often limited to a single level of granularity, such as function calls; and (3) there is a lack of the interpretation semantics about the graphs. In this dissertation, we propose a novel framework, called CodEx, to facilitate and accelerate program comprehension. CodEx enables top-down and bottom-up analysis of the system's call graph and its execution paths for an enhanced program comprehension experience. Specifically, the proposed framework is designed to cope with the following techniques: multi-level graph abstraction using a coarsening technique, hierarchical clustering to represent the call graph into subgraphs (i.e., multi-levels of granularity), and interactive visual exploration of the graphs at different levels of abstraction. Moreover, we are also worked on building semantics of software systems using NLP and machine learning, including topic modeling, to interpret the meaning of the abstraction levels of the call graph.Introduction -- Multi-Level Call Graph for Program Comprehension -- Static Trace Clustering: Single-Level Approach -- Static Trace Clustering: Multi-Level Approach -- Topic Modeling for Cluster Analysis -- Visual Exploration of Software Clustered Traces -- Conclusion and Feature Work -- Appendi

The Dune framework: Basic concepts and recent developments

This paper presents the basic concepts and the module structure of the Distributed and Unified Numerics Environment and reflects on recent developments and general changes that happened since the release of the first Dune version in 2007 and the main papers describing that state Bastian etal. (2008a, 2008b). This discussion is accompanied with a description of various advanced features, such as coupling of domains and cut cells, grid modifications such as adaptation and moving domains, high order discretizations and node level performance, non-smooth multigrid methods, and multiscale methods. A brief discussion on current and future development directions of the framework concludes the paper

Computer Aided Verification

This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

NASA high performance computing and communications program

The National Aeronautics and Space Administration's HPCC program is part of a new Presidential initiative aimed at producing a 1000-fold increase in supercomputing speed and a 100-fold improvement in available communications capability by 1997. As more advanced technologies are developed under the HPCC program, they will be used to solve NASA's 'Grand Challenge' problems, which include improving the design and simulation of advanced aerospace vehicles, allowing people at remote locations to communicate more effectively and share information, increasing scientist's abilities to model the Earth's climate and forecast global environmental trends, and improving the development of advanced spacecraft. NASA's HPCC program is organized into three projects which are unique to the agency's mission: the Computational Aerosciences (CAS) project, the Earth and Space Sciences (ESS) project, and the Remote Exploration and Experimentation (REE) project. An additional project, the Basic Research and Human Resources (BRHR) project exists to promote long term research in computer science and engineering and to increase the pool of trained personnel in a variety of scientific disciplines. This document presents an overview of the objectives and organization of these projects as well as summaries of individual research and development programs within each project