Slides from Software Testing Techniques course offered at Kansas State University in Spring'16 and Spring'17. Entire course material can be found at https://github.com/rvprasad/software-testing-course.
This document summarizes Seminar #3 on block ciphers. It covers the following topics: applications of block ciphers; Galois fields and operations with polynomials; the Feistel scheme; GOST 28147-89 and its key length, crypto unit size, and number of cycles; AES including its key lengths, crypto unit size, number of cycles, and details of its SubBytes, ShiftRows, MixColumns, and AddRoundKey stages; and encryption algorithms application schemes such as ECB, cipher block chaining, cipher feedback, output feedback, and XTS modes.
This document contains several examples of using Mason's Rule to calculate transfer functions from signal flow graphs. The examples demonstrate applying Mason's Rule to calculate loop gains, forward path gains, non-touching loops, and the overall transfer function. Signal flow graphs are constructed from block diagrams and the steps of Mason's Rule are systematically worked through.
block diagram representation of control systemsAhmed Elmorsy
This document provides an introduction to block diagram representation of control systems. It discusses how block diagrams provide a pictorial representation of the relationships between elements in a system using blocks and arrows. The blocks represent system elements or operations, and the arrows represent the direction of signal or information flow. Specific topics covered include summing points, takeoff points, examples of representing equations as block diagrams, and canonical forms.
This presnetation gives complete idea about block diagram representation and reduction techniques to find transfer function. Also gives complete idea about Signal flow graph method to find transfer function.
This document provides an overview of signal flow graphs including:
- Definitions and terminology of signal flow graphs
- Examples of constructing signal flow graphs from equations and block diagrams
- Mason's gain formula for calculating the transfer function of a system from its signal flow graph representation in 3 sentences or less
- Examples are provided to demonstrate applying Mason's gain formula to calculate transfer functions from given signal flow graphs.
This document discusses block diagram representation in control system engineering. It introduces block diagrams as a pictorial representation of a system using blocks and arrows to show inputs, outputs, and signal flow. Examples are provided to illustrate how to represent mathematical equations using block diagrams and techniques for reducing complex block diagrams, such as combining blocks in series or parallel and moving summing and pickoff points. The goal of reduction is to transform the block diagram into canonical form to determine the system's characteristic equation.
This document proposes an approach called redundancy-aware maximal clique enumeration (MCE) to generate concise summaries of maximal cliques in graphs. It introduces the notion of a τ-visible summary, which guarantees each clique is covered by the summary at least τ. The approach uses sampling and early pruning to efficiently find small τ-visible summaries, providing a compact representation while preserving information. Experimental results on real-world networks show the summaries are much smaller in size and faster to generate than enumerating all cliques, with only small losses in reported top-k clique quality.
This document discusses signal flow graphs and Mason's rule for calculating transfer functions from such graphs. It defines key terms used in signal flow graphs like nodes, branches, paths, loops, and gains. Mason's rule is presented as a way to relate a signal flow graph to the simultaneous equations of the system to determine the overall transfer function T. The formula for T is given as the sum of the products of the forward path gains and the characteristic function Δ over the number of paths. An example is worked through to demonstrate applying Mason's rule.
This document summarizes Seminar #3 on block ciphers. It covers the following topics: applications of block ciphers; Galois fields and operations with polynomials; the Feistel scheme; GOST 28147-89 and its key length, crypto unit size, and number of cycles; AES including its key lengths, crypto unit size, number of cycles, and details of its SubBytes, ShiftRows, MixColumns, and AddRoundKey stages; and encryption algorithms application schemes such as ECB, cipher block chaining, cipher feedback, output feedback, and XTS modes.
This document contains several examples of using Mason's Rule to calculate transfer functions from signal flow graphs. The examples demonstrate applying Mason's Rule to calculate loop gains, forward path gains, non-touching loops, and the overall transfer function. Signal flow graphs are constructed from block diagrams and the steps of Mason's Rule are systematically worked through.
block diagram representation of control systemsAhmed Elmorsy
This document provides an introduction to block diagram representation of control systems. It discusses how block diagrams provide a pictorial representation of the relationships between elements in a system using blocks and arrows. The blocks represent system elements or operations, and the arrows represent the direction of signal or information flow. Specific topics covered include summing points, takeoff points, examples of representing equations as block diagrams, and canonical forms.
This presnetation gives complete idea about block diagram representation and reduction techniques to find transfer function. Also gives complete idea about Signal flow graph method to find transfer function.
This document provides an overview of signal flow graphs including:
- Definitions and terminology of signal flow graphs
- Examples of constructing signal flow graphs from equations and block diagrams
- Mason's gain formula for calculating the transfer function of a system from its signal flow graph representation in 3 sentences or less
- Examples are provided to demonstrate applying Mason's gain formula to calculate transfer functions from given signal flow graphs.
This document discusses block diagram representation in control system engineering. It introduces block diagrams as a pictorial representation of a system using blocks and arrows to show inputs, outputs, and signal flow. Examples are provided to illustrate how to represent mathematical equations using block diagrams and techniques for reducing complex block diagrams, such as combining blocks in series or parallel and moving summing and pickoff points. The goal of reduction is to transform the block diagram into canonical form to determine the system's characteristic equation.
This document proposes an approach called redundancy-aware maximal clique enumeration (MCE) to generate concise summaries of maximal cliques in graphs. It introduces the notion of a τ-visible summary, which guarantees each clique is covered by the summary at least τ. The approach uses sampling and early pruning to efficiently find small τ-visible summaries, providing a compact representation while preserving information. Experimental results on real-world networks show the summaries are much smaller in size and faster to generate than enumerating all cliques, with only small losses in reported top-k clique quality.
This document discusses signal flow graphs and Mason's rule for calculating transfer functions from such graphs. It defines key terms used in signal flow graphs like nodes, branches, paths, loops, and gains. Mason's rule is presented as a way to relate a signal flow graph to the simultaneous equations of the system to determine the overall transfer function T. The formula for T is given as the sum of the products of the forward path gains and the characteristic function Δ over the number of paths. An example is worked through to demonstrate applying Mason's rule.
Signal Flow Graph, SFG and Mason Gain Formula, Example solved with Masson Gai...Waqas Afzal
Basic Properties of SFG
Definitions of SFG Terms
SFG Algebra
Relation between SFG and block diagram
Mason Gain Formula
Example solved with Masson Gain Formula
This document outlines a lecture on block diagrams for a control systems engineering course. It begins with an introduction to block diagrams and their use in representing subsystems and combining multiple subsystems. It provides examples of subsystems for automobile control and antenna control. The document then discusses mathematical modeling of systems using transfer functions. It covers concepts such as cascaded systems, feedback forms, and simplifying block diagrams. Finally, it provides examples of simplifying complex block diagrams and modeling a shuttle pitch control system using block diagrams.
The document discusses signal flow graphs (SFG), which provide a graphical representation of the relationship between variables in a system without needing to reduce block diagrams. An SFG consists of nodes and directed branches and can only represent linear systems. The basic elements of an SFG include branches, nodes, paths, loops, and different types of loops. Mason's gain formula is presented for calculating the transfer function of a system from its SFG. Examples are given to demonstrate how to derive an SFG from a block diagram and simultaneous equations, and how to use Mason's formula to solve for paths and loops.
Uwe Friedrichsen - CRDT und mehr - über extreme Verfügbarkeit und selbstheile...AboutYouGmbH
The document discusses conflict-free replicated data types (CRDTs), which are data structures designed to achieve maximum availability in distributed systems where strict consistency is not possible. It describes how CRDTs use state-based or operation-based approaches to allow for eventual consistency while preventing inconsistencies. Examples of CRDT implementations of counters, sets, registers and other data types are provided, along with discussions of their advantages and limitations compared to strict consistency models.
This document provides an overview of signal flow graphs and Mason's rule for calculating transfer functions from such graphs. It begins with definitions of key signal flow graph concepts like nodes, branches, paths, and loops. It then gives examples of constructing signal flow graphs from sets of simultaneous equations and converting block diagrams. Mason's rule is explained as providing the transfer function from a single formula involving the forward path gains and graph determinants, avoiding successive block diagram reductions. Finally, the document works through two examples applying Mason's rule to calculate transfer functions from given signal flow graphs.
Control system concepts by using matlabCharltonInao1
This document introduces control system concepts and how they can be analyzed using MATLAB. It discusses open and closed loop systems, Laplace transforms, state variable approaches, and MATLAB commands to analyze systems. Examples are provided on determining transfer functions, computing step and frequency responses, plotting root loci and Bode diagrams, and performing time and frequency domain analyses. Block diagrams can be reduced and overall transfer functions obtained through series and parallel combinations.
This document presents information about the shortest path problem in graphs. It defines key graph terms like vertices, edges, and discusses weighted, directed, and undirected graphs. It provides an example of finding the shortest path between two vertices in a graph using Dijkstra's algorithm and walks through the steps of running the algorithm on a sample graph to find the shortest path between vertices 1 and 9.
This document summarizes a lecture on algorithm development using pseudocode. It discusses representing algorithms with pseudocode, the different types of algorithmic operations including sequential, conditional, and iterative operations. Examples are provided of algorithms to solve problems like sequential search, finding the maximum value, and pattern matching. Pseudocode allows designing algorithms in a readable way that can then be translated to an actual programming language.
asymptotic analysis and insertion sort analysisAnindita Kundu
This document discusses asymptotic analysis of algorithms. It introduces key concepts like algorithms, data structures, best/average/worst case running times, and asymptotic notations like Big-O, Big-Omega, and Big-Theta. These notations are used to describe the long-term growth rates of functions and provide upper/lower/tight bounds on the running time of algorithms as the input size increases. Examples show how to analyze the asymptotic running time of algorithms like insertion sort, which is O(n^2) in the worst case but O(n) in the best case.
This document discusses bi-connected components in graphs. It defines an articulation point as a vertex in a connected graph whose removal would disconnect the graph. A bi-connected component is a maximal subgraph that contains no articulation points. The document presents algorithms for identifying articulation points and bi-connected components in a graph using depth-first search (DFS). It introduces the concepts of tree edges, back edges, forward edges and cross edges in a DFS tree and explains how to use these edge types to determine if a vertex is an articulation point based on the minimum discovery time of its descendant vertices.
This document provides an overview of signal flow graphs. It defines key concepts like nodes, branches, paths, loops, and loop gains. Examples are given to illustrate these concepts, such as a simple voltage-current-resistance circuit and its corresponding signal flow graph. Different types of nodes, paths, and loops are defined, including forward paths, self loops, and non-touching loops. The document serves as an introduction to representing physical systems as signal flow graphs.
The document describes bucket sort and radix sort algorithms. It provides examples to illustrate how each algorithm works. Specifically:
1) Bucket sort works by counting the number of objects that fall into multiple "buckets" based on a certain value like size or weight. It then places the objects in the correct order.
2) Radix sort sorts data based on individual digits of a number, starting from the least significant digit to the most significant. It repeatedly performs bucket sorts on each digit to fully sort the data.
3) Examples show the step-by-step process of bucket sort and LSD radix sort on sample input data to produce a sorted output.
The document contains 10 multiple choice questions about computer science concepts like sorting algorithms, data structure operations, algorithm time complexities, file systems, and operating system functions. The questions cover topics like the fastest sorting algorithm for pre-sorted data, the average number of comparisons in sequential search, data structures that use push/pop and queue operations, examples of linear time algorithms, disk allocation methods, file types supported by systems, file access methods, and components managed by an operating system.
The document discusses signal flow graphs (SFG), which provide an alternative representation to block diagrams. SFGs depict the flow of signals through a system using directed branches connecting nodes. Important elements include input/output nodes, branches, loops, and path/loop gains. Mason's rule can be used to derive the transfer function of a system from its SFG. It involves calculating the determinant and individual path determinants based on loop gains. Examples show applying Mason's rule to calculate a transfer function and converting a block diagram into an equivalent SFG.
This document discusses signal flow graphs and Mason's gain formula. It begins by explaining how to convert a block diagram into an equivalent signal flow graph by labeling summing points and take-off points, assigning nodes, and connecting nodes with associated gains. An example is provided. Mason's rule for reducing a signal flow graph to a transfer function is introduced. It uses the net gain formula that considers forward paths, loop gains, and non-touching loops. Another example applies Mason's rule to calculate the transfer function. In the end, some references on the topic are listed.
This document discusses techniques for reducing block diagrams and signal flow graphs that represent complex control systems composed of multiple interconnected subsystems into a single transfer function. It introduces block diagram elements like summing junctions and pickoff points used to represent interconnections. Three methods for reducing block diagrams to an equivalent transfer function are presented: cascade form, parallel form, and feedback form. Rules are provided for moving blocks around diagrams to establish these familiar forms. Signal flow graphs are introduced as an alternative representation and Mason's rule is outlined as a method for reducing a signal flow graph to a transfer function. Examples are worked through to demonstrate these reduction techniques.
This presentation educates you about R - Time Series Analysis with its Following description parameters using basic syntax, The Time series chart, Different Time Intervals and Multiple Time Series with The Multiple Time series chart.
For more topics stay tuned with Learnbay.
Dijkstra's algorithm is used to find the shortest paths from a source node to all other nodes in a network. It works by marking all nodes as tentative with initial distances from the source set to 0 and others to infinity. It then extracts the closest node, adds it to the shortest path tree, and relaxes distances of its neighbors. This process repeats until all nodes are processed. When applied to the example network, Dijkstra's algorithm finds the shortest path from node A to all others to be A-B=4, A-C=6, A-D=8, A-E=7, A-F=7, A-G=7, and A-H=9.
This document contains 14 multiple choice questions related to digital logic circuits and Boolean algebra. The questions cover topics such as truth tables, logic gates, logic families, shift registers, Boolean expressions, and vector systems. Each question has 4 possible answer choices.
The document discusses the shortest path problem and Dijkstra's algorithm for solving it in graphs with non-negative edge weights. It defines the shortest path problem, explains that it is well-defined for non-negative graphs but not graphs with negative edge weights or cycles. It then describes Dijkstra's algorithm, how it works by iteratively finding the shortest path from the source to each vertex, and provides pseudocode for its implementation.
White Box testing by Pankaj Thakur, NITTTR ChandigarhPankaj Thakur
The document provides an overview of white box testing techniques. It begins with an introduction to white box testing and its various forms. It then compares white box and black box testing and discusses cyclomatic complexity. The document outlines several white box testing techniques including control flow testing, data flow testing, slice based testing, and mutation testing. It discusses the advantages and disadvantages of white box testing and provides examples to illustrate control flow testing, branch coverage, condition coverage, and path coverage. The document also briefly covers loop testing and data flow testing.
White box testing involves close examination of a program's internal structure and logic flows. Test cases are designed to exercise specific conditions and loops by following logical paths through the code. This ensures all independent paths, logical decisions, loops, and internal data structures are tested. Basis path testing is a white box technique that uses flow graphs and cyclomatic complexity to identify the minimum number of test cases needed to guarantee every statement is executed at least once. Cyclomatic complexity represents the number of independent paths in a program and is used to derive a basis set of test cases that force execution of every path.
Signal Flow Graph, SFG and Mason Gain Formula, Example solved with Masson Gai...Waqas Afzal
Basic Properties of SFG
Definitions of SFG Terms
SFG Algebra
Relation between SFG and block diagram
Mason Gain Formula
Example solved with Masson Gain Formula
This document outlines a lecture on block diagrams for a control systems engineering course. It begins with an introduction to block diagrams and their use in representing subsystems and combining multiple subsystems. It provides examples of subsystems for automobile control and antenna control. The document then discusses mathematical modeling of systems using transfer functions. It covers concepts such as cascaded systems, feedback forms, and simplifying block diagrams. Finally, it provides examples of simplifying complex block diagrams and modeling a shuttle pitch control system using block diagrams.
The document discusses signal flow graphs (SFG), which provide a graphical representation of the relationship between variables in a system without needing to reduce block diagrams. An SFG consists of nodes and directed branches and can only represent linear systems. The basic elements of an SFG include branches, nodes, paths, loops, and different types of loops. Mason's gain formula is presented for calculating the transfer function of a system from its SFG. Examples are given to demonstrate how to derive an SFG from a block diagram and simultaneous equations, and how to use Mason's formula to solve for paths and loops.
Uwe Friedrichsen - CRDT und mehr - über extreme Verfügbarkeit und selbstheile...AboutYouGmbH
The document discusses conflict-free replicated data types (CRDTs), which are data structures designed to achieve maximum availability in distributed systems where strict consistency is not possible. It describes how CRDTs use state-based or operation-based approaches to allow for eventual consistency while preventing inconsistencies. Examples of CRDT implementations of counters, sets, registers and other data types are provided, along with discussions of their advantages and limitations compared to strict consistency models.
This document provides an overview of signal flow graphs and Mason's rule for calculating transfer functions from such graphs. It begins with definitions of key signal flow graph concepts like nodes, branches, paths, and loops. It then gives examples of constructing signal flow graphs from sets of simultaneous equations and converting block diagrams. Mason's rule is explained as providing the transfer function from a single formula involving the forward path gains and graph determinants, avoiding successive block diagram reductions. Finally, the document works through two examples applying Mason's rule to calculate transfer functions from given signal flow graphs.
Control system concepts by using matlabCharltonInao1
This document introduces control system concepts and how they can be analyzed using MATLAB. It discusses open and closed loop systems, Laplace transforms, state variable approaches, and MATLAB commands to analyze systems. Examples are provided on determining transfer functions, computing step and frequency responses, plotting root loci and Bode diagrams, and performing time and frequency domain analyses. Block diagrams can be reduced and overall transfer functions obtained through series and parallel combinations.
This document presents information about the shortest path problem in graphs. It defines key graph terms like vertices, edges, and discusses weighted, directed, and undirected graphs. It provides an example of finding the shortest path between two vertices in a graph using Dijkstra's algorithm and walks through the steps of running the algorithm on a sample graph to find the shortest path between vertices 1 and 9.
This document summarizes a lecture on algorithm development using pseudocode. It discusses representing algorithms with pseudocode, the different types of algorithmic operations including sequential, conditional, and iterative operations. Examples are provided of algorithms to solve problems like sequential search, finding the maximum value, and pattern matching. Pseudocode allows designing algorithms in a readable way that can then be translated to an actual programming language.
asymptotic analysis and insertion sort analysisAnindita Kundu
This document discusses asymptotic analysis of algorithms. It introduces key concepts like algorithms, data structures, best/average/worst case running times, and asymptotic notations like Big-O, Big-Omega, and Big-Theta. These notations are used to describe the long-term growth rates of functions and provide upper/lower/tight bounds on the running time of algorithms as the input size increases. Examples show how to analyze the asymptotic running time of algorithms like insertion sort, which is O(n^2) in the worst case but O(n) in the best case.
This document discusses bi-connected components in graphs. It defines an articulation point as a vertex in a connected graph whose removal would disconnect the graph. A bi-connected component is a maximal subgraph that contains no articulation points. The document presents algorithms for identifying articulation points and bi-connected components in a graph using depth-first search (DFS). It introduces the concepts of tree edges, back edges, forward edges and cross edges in a DFS tree and explains how to use these edge types to determine if a vertex is an articulation point based on the minimum discovery time of its descendant vertices.
This document provides an overview of signal flow graphs. It defines key concepts like nodes, branches, paths, loops, and loop gains. Examples are given to illustrate these concepts, such as a simple voltage-current-resistance circuit and its corresponding signal flow graph. Different types of nodes, paths, and loops are defined, including forward paths, self loops, and non-touching loops. The document serves as an introduction to representing physical systems as signal flow graphs.
The document describes bucket sort and radix sort algorithms. It provides examples to illustrate how each algorithm works. Specifically:
1) Bucket sort works by counting the number of objects that fall into multiple "buckets" based on a certain value like size or weight. It then places the objects in the correct order.
2) Radix sort sorts data based on individual digits of a number, starting from the least significant digit to the most significant. It repeatedly performs bucket sorts on each digit to fully sort the data.
3) Examples show the step-by-step process of bucket sort and LSD radix sort on sample input data to produce a sorted output.
The document contains 10 multiple choice questions about computer science concepts like sorting algorithms, data structure operations, algorithm time complexities, file systems, and operating system functions. The questions cover topics like the fastest sorting algorithm for pre-sorted data, the average number of comparisons in sequential search, data structures that use push/pop and queue operations, examples of linear time algorithms, disk allocation methods, file types supported by systems, file access methods, and components managed by an operating system.
The document discusses signal flow graphs (SFG), which provide an alternative representation to block diagrams. SFGs depict the flow of signals through a system using directed branches connecting nodes. Important elements include input/output nodes, branches, loops, and path/loop gains. Mason's rule can be used to derive the transfer function of a system from its SFG. It involves calculating the determinant and individual path determinants based on loop gains. Examples show applying Mason's rule to calculate a transfer function and converting a block diagram into an equivalent SFG.
This document discusses signal flow graphs and Mason's gain formula. It begins by explaining how to convert a block diagram into an equivalent signal flow graph by labeling summing points and take-off points, assigning nodes, and connecting nodes with associated gains. An example is provided. Mason's rule for reducing a signal flow graph to a transfer function is introduced. It uses the net gain formula that considers forward paths, loop gains, and non-touching loops. Another example applies Mason's rule to calculate the transfer function. In the end, some references on the topic are listed.
This document discusses techniques for reducing block diagrams and signal flow graphs that represent complex control systems composed of multiple interconnected subsystems into a single transfer function. It introduces block diagram elements like summing junctions and pickoff points used to represent interconnections. Three methods for reducing block diagrams to an equivalent transfer function are presented: cascade form, parallel form, and feedback form. Rules are provided for moving blocks around diagrams to establish these familiar forms. Signal flow graphs are introduced as an alternative representation and Mason's rule is outlined as a method for reducing a signal flow graph to a transfer function. Examples are worked through to demonstrate these reduction techniques.
This presentation educates you about R - Time Series Analysis with its Following description parameters using basic syntax, The Time series chart, Different Time Intervals and Multiple Time Series with The Multiple Time series chart.
For more topics stay tuned with Learnbay.
Dijkstra's algorithm is used to find the shortest paths from a source node to all other nodes in a network. It works by marking all nodes as tentative with initial distances from the source set to 0 and others to infinity. It then extracts the closest node, adds it to the shortest path tree, and relaxes distances of its neighbors. This process repeats until all nodes are processed. When applied to the example network, Dijkstra's algorithm finds the shortest path from node A to all others to be A-B=4, A-C=6, A-D=8, A-E=7, A-F=7, A-G=7, and A-H=9.
This document contains 14 multiple choice questions related to digital logic circuits and Boolean algebra. The questions cover topics such as truth tables, logic gates, logic families, shift registers, Boolean expressions, and vector systems. Each question has 4 possible answer choices.
The document discusses the shortest path problem and Dijkstra's algorithm for solving it in graphs with non-negative edge weights. It defines the shortest path problem, explains that it is well-defined for non-negative graphs but not graphs with negative edge weights or cycles. It then describes Dijkstra's algorithm, how it works by iteratively finding the shortest path from the source to each vertex, and provides pseudocode for its implementation.
White Box testing by Pankaj Thakur, NITTTR ChandigarhPankaj Thakur
The document provides an overview of white box testing techniques. It begins with an introduction to white box testing and its various forms. It then compares white box and black box testing and discusses cyclomatic complexity. The document outlines several white box testing techniques including control flow testing, data flow testing, slice based testing, and mutation testing. It discusses the advantages and disadvantages of white box testing and provides examples to illustrate control flow testing, branch coverage, condition coverage, and path coverage. The document also briefly covers loop testing and data flow testing.
White box testing involves close examination of a program's internal structure and logic flows. Test cases are designed to exercise specific conditions and loops by following logical paths through the code. This ensures all independent paths, logical decisions, loops, and internal data structures are tested. Basis path testing is a white box technique that uses flow graphs and cyclomatic complexity to identify the minimum number of test cases needed to guarantee every statement is executed at least once. Cyclomatic complexity represents the number of independent paths in a program and is used to derive a basis set of test cases that force execution of every path.
- The document summarizes techniques for slicing object-oriented programs. It discusses static and dynamic slicing, and limitations of previous approaches.
- It proposes a new intermediate representation called the Object-Oriented System Dependence Graph (OSDG) to more precisely capture dependencies in object-oriented programs. The OSDG explicitly represents data members of objects.
- An edge-marking algorithm is presented for efficiently performing dynamic slicing of object-oriented programs using the OSDG. This avoids recomputing the entire slice after each statement.
The document discusses techniques for improving code through transformations, including eliminating common subexpressions, copy propagation, detecting loop-invariant computations, performing code motion, and eliminating induction variables. Code transformations aim to reduce code size and running time by taking advantage of properties like invariant values to optimize programs without changing their meaning or output. Algorithms described include analyzing flow graphs, data flow, and control flow to identify optimization opportunities and apply transformations correctly and safely.
This document summarizes Praveen Varma's seminar on slicing object-oriented programs. It introduces different types of program slicing including static, dynamic, backward, and forward slicing. It discusses approaches to slicing like control flow graph based and dependence graph based methods. It also covers interprocedural slicing, static and dynamic slicing of object-oriented programs, and applications of program slicing. Limitations of existing work on object-oriented program slicing are presented along with the motivation, objectives, and work done on developing more efficient slicing techniques for object-oriented programs.
This document discusses program slicing techniques for object-oriented programs. It outlines previous approaches to static and dynamic slicing of OOPs and their limitations. The key points are:
1) Early approaches used system dependence graphs but had issues distinguishing between objects and imprecisely modeled polymorphism.
2) Newer work proposes an object-oriented system dependence graph (OSDG) that models objects as trees with members to address earlier limitations.
3) The document also discusses developing efficient algorithms for dynamic slicing of concurrent object-oriented programs.
Queuing theory and traffic analysis in depthIdcIdk1
This document provides a summary of concepts in queuing theory and network traffic analysis. It discusses queuing theory concepts like Little's Law, M/M/1 queues, and Kendall's notation. It then covers an empirical study of router delay that models delays using a fluid queue and reports on busy period metrics. Finally, it discusses the concept of network traffic self-similarity found in measurements of Ethernet LAN traffic.
White box testing involves closely examining the internal logic and structure of code. It focuses on testing individual program paths by exercising conditions, loops, and logical decisions. Test cases are designed to ensure each path is tested at least once to cover all statements. Cyclomatic complexity measures the number of independent paths in a program and provides an upper bound for the number of test cases needed. Flow graphs are used to represent program control flow and determine the cyclomatic complexity and basis path testing set.
This document discusses convolutional codes. It defines basic concepts like constraint length and generator polynomials that define convolutional codes. It describes representations of convolutional codes using state diagrams and trellis diagrams. It also discusses decoding convolutional codes using the Viterbi algorithm, which finds the most likely path through the trellis. The document concludes by discussing properties of convolutional codes like free distance, which is the minimum Hamming distance between codewords.
Data structures assignmentweek4b.pdfCI583 Data StructureOllieShoresna
The document discusses resiliency in file systems. Early file systems could become corrupted after crashes, losing unwritten file modifications or damaging unrelated files. This was because data structures describing the entire file system, like the superblock and inode list, could become corrupted. Modern file systems employ techniques like journaling and shadow paging to ensure all changes are written atomically and the file system remains consistent even after crashes.
The document discusses various software testing techniques for object-oriented systems, including white-box testing, black-box testing, and object-oriented specific methods. It covers topics such as unit testing, integration testing, equivalence partitioning, boundary value analysis, and fault-based versus scenario-based testing. The overall goal is to uncover errors in classes, class collaboration, and subsystem communication through different levels of testing.
Generalized Linear Models in Spark MLlib and SparkR by Xiangrui MengSpark Summit
Generalized linear models (GLMs) are a class of models that include linear regression, logistic regression, and other forms. GLMs are implemented in both MLlib and SparkR in Spark. They support various solvers like gradient descent, L-BFGS, and iteratively re-weighted least squares. Performance is optimized through techniques like sparsity, tree aggregation, and avoiding unnecessary data copies. Future work includes better handling of categoricals, more model statistics, and model parallelism.
Generalized Linear Models in Spark MLlib and SparkRDatabricks
Generalized linear models (GLMs) unify various statistical models such as linear regression and logistic regression through the specification of a model family and link function. They are widely used in modeling, inference, and prediction with applications in numerous fields. In this talk, we will summarize recent community efforts in supporting GLMs in Spark MLlib and SparkR. We will review supported model families, link functions, and regularization types, as well as their use cases, e.g., logistic regression for classification and log-linear model for survival analysis. Then we discuss the choices of solvers and their pros and cons given training datasets of different sizes, and implementation details in order to match R’s model output and summary statistics. We will also demonstrate the APIs in MLlib and SparkR, including R model formula support, which make building linear models a simple task in Spark. This is a joint work with Eric Liang, Yanbo Liang, and some other Spark contributors.
Modeling and Querying Metadata in the Semantic Sensor Web: stRDF and stSPARQLKostis Kyzirakos
This document discusses modeling and querying metadata in the semantic sensor web using stRDF and stSPARQL. It introduces stRDF, an extension of RDF that uses linear constraints to represent spatial and temporal metadata. It also introduces stSPARQL, an extension of SPARQL with spatial and temporal query capabilities. The talk outlines the motivation, data model, query language, implementation details, and examples of querying sensor metadata and observations.
These slides gives you information about path testing and data flow testing in software testing. These slides will be helpful for Engineering, CSE students.
This document summarizes a student project on OFDM transmitters and receivers. It includes an introduction to OFDM that describes its use of orthogonal subcarriers. It also compares single carrier modulation to multi-carrier modulation using OFDM. The document outlines the basic OFDM transmitter and receiver block diagrams. It discusses the constellation mapper, IFFT block, cyclic prefix, and design approaches for these blocks. Simulation results are presented comparing transmitted and received signals. BER performance is evaluated for different modulation schemes like QPSK and QAM. The document concludes that OFDM provides high bandwidth efficiency and overcomes interference through the IFFT and cyclic prefix.
This document summarizes computational studies of two path-based traffic assignment algorithms: the disaggregate simplicial decomposition (DSD) algorithm and the gradient projection (GP) algorithm. The study used a large-scale real network in Chicago and five randomly generated networks. Results showed that the GP algorithm performed better than both versions of the DSD algorithm on all networks, finding solutions faster with fewer iterations. The GP algorithm was more efficient by maintaining a smaller set of active paths and avoiding expensive line searches through second derivative information. While DSD could find near-optimal solutions quickly, it took more time overall and maintained a larger set of paths in each iteration.
The document discusses various whitebox testing techniques including statement coverage, branch coverage, condition coverage, path coverage, and data flow-based testing. Statement coverage requires designing test cases such that every statement in a program is executed at least once. Branch coverage requires test cases where different branch conditions are given true and false values. Path coverage requires test cases such that all linearly independent paths in a program are executed based on the program's control flow graph. Data flow-based testing focuses on connections between variable definitions and uses.
Similar to Code Coverage [9] - Software Testing Techniques (CIS640) (20)
Are free Android app security analysis tools effective in detecting known vul...Venkatesh Prasad Ranganath
An evaluation of the effectiveness of 14 Android security analysis tools in detecting 42 known vulnerabilities spread across different aspects of apps, e.g., crypto, ICC, web.
It is also the first evaluation of representativess of vulnerability benchmarks -- is the manifestation of the vulnerability in the benchmark similar to its manifestation in real world apps?
An independent and comparative evaluation of the representativeness of four Android app vulnerability benchmark suites: DroidBench, Ghera, IccBench, and UBCBench.
The document discusses a study conducted on the Beocat HPC cluster at Kansas State University to understand why users terminate jobs early. The study found that user terminated jobs accounted for around 10% of total CPU time and 12.75% of user wait time, representing significant wasted resources. The top reasons for job termination included exploring the system, system errors, jobs not finishing on time, and jobs converging or not converging earlier than expected. The study suggests ways to address the top reasons and reduce wastage through techniques like improving system reliability, helping users estimate job runtimes better, and automating convergence detection. Repeating such studies on other clusters could help understand wastage in different HPC environments.
Slides from Software Testing Techniques course offered at Kansas State University in Spring'16 and Spring'17. Entire course material can be found at https://github.com/rvprasad/software-testing-course.
Slides from Software Testing Techniques course offered at Kansas State University in Spring'16 and Spring'17. Entire course material can be found at https://github.com/rvprasad/software-testing-course.
Slides from Software Testing Techniques course offered at Kansas State University in Spring'16 and Spring'17. Entire course material can be found at https://github.com/rvprasad/software-testing-course.
Slides from Software Testing Techniques course offered at Kansas State University in Spring'16 and Spring'17. Entire course material can be found at https://github.com/rvprasad/software-testing-course.
Slides from Software Testing Techniques course offered at Kansas State University in Spring'16 and Spring'17. Entire course material can be found at https://github.com/rvprasad/software-testing-course.
Slides from Software Testing Techniques course offered at Kansas State University in Spring'16 and Spring'17. Entire course material can be found at https://github.com/rvprasad/software-testing-course.
Slides from Software Testing Techniques course offered at Kansas State University in Spring'16 and Spring'17. Entire course material can be found at https://github.com/rvprasad/software-testing-course.
The document discusses the skills needed for testing, including understanding code, interfaces, execution environments, logic, and technical writing. It then provides examples of testing a sort function by checking for exceptions with different input values and verifying the expected output. Finally, it discusses what needs to be known to implement and test a sort function, such as the sorting order, valid/invalid inputs, and intended behavior.
How does you test that USB 3 driver stack in Windows 8 was similar to USB 2 driver stack in Windows 7 in the context of serving a USB 2 device? By capturing the interaction logs between the device driver and the USB drivers and comparing the logs by abstracting them as sets of patterns.
This short talk presents some observations and opportunities in the space of using
data analysis to enable, accomplish, and improve software engineering tasks such
as testing.
This document provides an overview of key concepts in data analytics, including:
1. It distinguishes between analytics, which uses analysis to make recommendations, and analysis.
2. Common purposes of data analysis are to confirm hypotheses or explore data through confirmatory or exploratory analysis.
3. The typical data analytics workflow involves 8 steps: identifying the issue, data collection/preparation, cleansing, transformation, analysis, validation, presentation, and making recommendations.
4. Important data preparation concepts covered include storage options, access and privacy considerations, representation formats, and data scales. Cleansing, transformation, and feature engineering techniques are also summarized.
5. Common analysis methods, validation approaches, and
Do you need to process sequential and structure data (e.g. structured logs)? Use off-the-shelf pattern mining techniques to mine patterns from data and use the mined patterns as features (in combination with classic data mining / machine learning techniques).
http://research.microsoft.com/apps/pubs/default.aspx?id=1883
http://research.microsoft.com/en-us/events/dapse2013/
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
2. A Program & its CFG
s0: z = input()
s1: x = input()
s2: if x > 5:
s3: y = x * 5
else:
s4: y = z / 5
s5: print(y)
s6: return
CFG is short for Control Flow Graph
s0
s1
s2
s4s3
s5
s6
Each node is a
statement
Each solid edge is
control flow edge
between two
statements
3. Node and Edge Coverage*
• Node (Statement) Coverage
• Fraction of graph nodes covered by tests
• Testing Goal: Every node should be executed at
least once
• (Control Flow) Edge Coverage
• Fraction of graph edges covered by tests
• Testing Goal: Every edge should be executed at
least once
4. • Path is a sequence of nodes in a graph
such that consecutive nodes in the path
are connected by a edge in the graph
• (Control Flow) Path Coverage
• Fraction of graph paths covered by tests
• How can we deal with programs with
loops, i.e. graphs with infinite number of
paths?
t0
t1
t2
t3
(Control Flow) Path Coverage*
5. • (Control Flow) Path Coverage — Testing Goal
• Every path between every pair of nodes should be
executed (may lead to redundancy)
• Every path between source and sinks should be
executed.
• Ideal for programs without loops
• Sufficient number of paths between source and sink
nodes are executed such that all edges are executed
• Every finite path between source and sinks should
executed such that each loop is executed at least
once (when all paths are considered)
• Good enough for programs with loops
(Control Flow) Path Coverage*
6. • (Control Flow) Path Coverage
• What about programs without sinks?
• Every finite path between source and
every node is executed with each
loop executed at least once (when all
paths are considered)
• What about infeasible paths?
(Control Flow) Path Coverage*
t0
t1
t2
t3
7. A Program & its DFG
s0: z = input()
s1: x = input()
s2: if x > 5:
s3: y = x * 5
else:
s4: y = z / 5
s5: print(y)
s6: return
DFG is short for Data Flow Graph
Each node is a
statement
Each solid edge is
the control flow
between two
statements
Each dashed edge is
the data flow between
two statements (from
definition of a variable
to its use)
s0
s1
s2
s4s3
s5
s6
z
x
x
y
y
8. Data Flow Coverage*
• Fraction of def-use edges covered by tests
• Testing Goal: Every def-use edge should be
executed at least once
• How well will this work in case of programs with
pointers and references?
• Can we detect all def-use edges?
• What about infeasible def-use edges?
• What about in case of programs written in OO
languages?
9. Which is better?
With 2 (red) paths, we get 100% node
and edge coverage but miss exercising
s2-s6 data flow edge
s1
s3s2
s4
s6s5
s7
10. Which is better?
With 2 (red) paths, we get 100%
node and edge coverage but miss
exercising s3-s6 data flow edge
s1
s3s2
s4
s6s5
s7
11. Which is better?
With 2 (red) paths, we get 100% data
flow coverage (s2-s6 and s3-s6) but
miss exercising s5 node
s1
s3s2
s4
s6s5
s7
14. Branch Coverage*
• Fraction of branches (edges) covered by tests
• Testing Goal: Every branch should be executed at
least once
• How does this relate to node and edge coverage?
15. Boolean Expressions
x > 0
x > 0 y < 0
and z > 10
or
x > 0 x > 0 and y < 0 or z > 10
Simple boolean
expressions contain no
logical connectives
Compound boolean
expressions contain
logical connectives
16. Condition Coverage*
• Fraction of boolean expression valuations covered
by tests
• Testing Goal:
• Every simple boolean expression should be
evaluated to both true and false
• Every compound boolean expression should be
evaluated to both true and false
17. Condition Coverage
• What about coupling between sub-expressions of a
compound expression?
• (x>0 and y) or (x<=0 and z)
• What about masking between sub-expressions of a
compound expression?
• (x>100 and y) where x ranges from 0 to 100