Control systems Unit-I (Dr.D.Lenine, RGMCET, Nandyal)Lenine D
This document provides an overview of control systems including objectives, outcomes, and an introduction to various concepts. It discusses open and closed loop systems, feedback types, mathematical modeling of mechanical and electrical systems, and transfer functions. Examples are provided to illustrate modeling of translational, rotational, and electrical systems. The document also covers electrical analogies of mechanical systems. The overall purpose is to introduce fundamental control system concepts and modeling techniques.
Generating Automated and Online Test Oracles for Simulink Models with Continu...Lionel Briand
This document presents an approach called SOCRaTes for automatically generating online test oracles for Simulink models of cyber-physical systems with continuous and uncertain behaviors. SOCRaTes takes as input a Simulink model, requirements specified in a restricted first-order logic language (RFOL), and test inputs. It generates test oracles in the form of Simulink blocks by translating the RFOL formulas. The generated oracles can check requirements online during simulation and handle parameter uncertainties. The document evaluates the approach on industrial case studies and shows that RFOL can capture real requirements, the translation to oracles is efficient, and online oracles save significant execution time.
Accenture testingchallenge casestudy Cgs test estimatesAmit Bhardwaj
The document provides estimates for testing the Centralized Global System. It estimates the total testing time to be 390 man hours or approximately 47 man days. It identifies 4 test cases that will cover all scenarios for this release: a home page test case, a workflow process test case, a centralized admin test case, and a timesheet extension test case. Estimates for writing and executing each test case are provided, totaling 68 man hours for writing and 72 man hours for execution. An additional 18 man hours is estimated for regression testing, bringing the total estimated time to 158 man hours.
Formal methods help improve the quality and reliability of software by providing proof of correctness. However, ensuring the correctness of verification tools that apply these formal methods, is itself a much harder problem. A typical way to justify the correctness is to provide soundness proofs based on semantic models. For program verifiers these soundness proofs are quite large and complex. In this thesis, we introduce certified reasoning to provide machine checked proofs of various components of an automated verification system. We develop new certified decision procedures (Omega++) and certified proofs (for compatible sharing) and integrate with an existing automated verification system (HIP/SLEEK). We show that certified reasoning improves the correctness and expressivity of automated verification without sacrificing on performance.
System Test Evaluation and Review TechniqueAniket Mitra
The document describes a Stochastic Modeling and Optimal Control approach called STERT (System Test Evaluation and Review Technique) for modeling and optimizing the system testing process. It presents system testing as a semi-Markov decision process where test execution times have a general distribution. The model accounts for complexity of test cases and allows a test manager to decide if additional investment should be made to meet schedule constraints. The optimal policy is to invest in test cases belonging to the most complex class and that are parent to other complex cases. Future work may extend the model to multiple testers and defect tracking.
Mining Assumptions for Software Components using Machine LearningLionel Briand
EPIcuRus is an approach to automatically generate assumptions for software components in cyber-physical systems modeled in Simulink. It uses machine learning techniques to mine assumptions from test case results. The approach includes generating tests cases using an important feature boundary test generation method, model checking candidate assumptions, and selecting the most informative safe assumptions. An evaluation on industrial case studies found it can learn non-vacuous assumptions for most requirements within a practical time limit, with the important feature boundary test generation performing best.
Presentation Description: Patient flow is a universal challenge in healthcare. Through a systematic evaluation of our cardiac catheterization lab operations (EMR assessment, metric development, capacity analysis using simulation), we found opportunities to reduce unnecessarily bedded cath lab patients by 8 per day on high-demand telemetry units at a large Midwestern hospital. Objective/Purpose: : Frequently, patients at Iowa Methodist Medical Center’s ED and ICU are waiting for a telemetry bed to become available. Patients who are either waiting for a cath procedure or have just had one, occupy nearly 50% of the available telemetry beds, many of whom do not require inpatient level of care. Inadequate, non-transparent cath lab operations and poor schedule adherence were signaled as reasons for why many patients are unnecessarily bedded. Objectives: Understand EMR usage; Scheduling / Reporting Capabilities Develop Sustainable Operations Metrics (KPI’s) Use Discrete Event Simulation to Model Various Improvement Scenarios Track impact to patient access and usage of inpatient telemetry beds Methods/Approach: : Methods/Approach Used: Understanding EMRs:24+ hours of observations of the practical use of our EMR for scheduling, documenting, and reporting. Participated in vendor meetings and webinars on current and alternative systems. Developing Metrics: pulled EMR data and developed 6 key metrics + a process for the ongoing delivery of charts and graphs for use by cath lab leadership. Discrete Event Simulation and Impact to Operations: Data was pulled and validated from EMRs, and Arena® DES was used to model over 20 scenarios for changes. Outputs were captured, compared, and selected for implementation. Results/Findings: : The following scenarios modeled using discrete event simulation had desired impacts: Limit outpatient elective cases to 3 on Mondays and Fridays Increase same day discharges of PCI patients Improve amount of cath proceduralists arriving at 7:00AM (or 7:30AM/8:00AM) Provide 8 hours of capacity to perform catheterizations for urgent patients and inpatients on the weekend Work to develop a process of sending same day elective outpatients to other underutilized cath lab within health system (<10 minutes away) Items 1-3 reduce inpatient bed usage by 5 per day; all 5 together result in a reduction of 8 occupied telemetry beds. Conclusion/Practical Implications: : Our analysis demonstrated the value of understanding workflow and information flow for making improvements. Information leads to transparency and ongoing reporting of performance is essential. Using simulation to model change allows for making large operational changes in a safe, virtual environment. We saw the downstream effects of our changes on the hospital overall with the potential to greatly impact telemetry bed usage and patient flow at UnityPoint Health – Des Moines.
Adding unit tests to the database deployment pipelineEduardo Piairo
This document discusses adding unit tests using tSQLt to the database deployment pipeline. tSQLt is a unit testing framework for SQL Server that allows writing T-SQL code as tests. Tests are run in transactions and objects can be isolated using mocking. The document covers installing tSQLt, writing and running tests, testing without data using mocking, asserts for validating tests, and integrating tSQLt into a continuous integration/delivery pipeline.
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competitions. This year the contest introduces new benchmarks to
assess the performance of JUnit testing tools on different types of
real-world software projects. Following on the statistical analyses
from the past contest work, we have extended it with the performance of the combined tool aiming to beat the human-made tests. Overall,
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This document discusses various types of software testing performed at different stages of the software development lifecycle. It describes component testing, integration testing, system testing, and acceptance testing. Component testing involves testing individual program units in isolation. Integration testing combines components and tests their interactions, starting small and building up. System testing evaluates the integrated system against functional and non-functional requirements. Acceptance testing confirms the system meets stakeholder needs.
The document discusses various types and stages of software testing in the software development lifecycle, including:
1. Component testing, the lowest level of testing done in isolation on individual software modules.
2. Integration testing in small increments to test communication between components and non-functional aspects.
3. System testing to test functional and non-functional requirements at the full system level, often done by an independent test group.
4. The document provides details on planning, techniques, and considerations for each type of testing in the software development and integration process.
The document discusses software testing in the software development lifecycle. It covers principles of testing at different stages of the lifecycle from component testing to integration testing to system testing and acceptance testing. It also discusses test planning, techniques for different types of testing including static vs dynamic testing, and management of the testing process.
The document discusses software testing over the lifecycle. It covers principles of testing, different testing techniques, and management of testing. It discusses testing at various stages of the lifecycle including component testing, integration testing, system testing, and acceptance testing. It also discusses test planning, documentation, and management.
The document discusses various topics related to software testing including:
1. It introduces different levels of testing in the software development lifecycle like component testing, integration testing, system testing and acceptance testing.
2. It discusses the importance of early test design and planning and its benefits like reducing costs and improving quality.
3. It provides examples of how not planning tests properly can increase costs due to bugs found late in the process, and outlines the typical costs involved in fixing bugs at different stages.
This paper proposes a multiple query optimization (MQO) scheme for change point detection (CPD) that can significantly reduce the number of operators needed. CPD is used to detect anomalies in time series data but requires tuning parameters, which leads to running multiple CPDs with different parameters. The paper identifies four patterns for sharing CPD operators between queries based on whether parameter values are the same. Experiments show the proposed MQO approach reduces the number of operators by up to 80% compared to running each CPD independently, thus improving performance. Integrating MQO with hardware accelerators is suggested as future work.
TDD is a design technique where tests are written before code to determine requirements. The Red-Green-Refactor process is followed: 1) Write a test that fails (red), 2) Write minimum code to pass the test (green), 3) Refactor code. TDD ensures quality, keeps code simple and testable, and allows for rapid change. Writing tests first helps design by clarifying requirements and preventing bugs.
Unit Testing Basics discusses the definition, types, benefits, and best practices of unit testing. The key points are:
- Unit tests verify small elements of code like classes or methods and have characteristics like being fast, isolated, repeatable, and self-checking.
- Different types of tests include unit tests, component tests, system tests, and functional tests which test at different scopes and have varying execution times.
- Well-designed unit tests provide benefits like documentation, error detection, and code improvements. Tests should be written before code using techniques like test-driven development.
- Frameworks like EasyMock can be used to create test doubles and mock objects to isolate the system under test and verify
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The document summarizes the 5th round of a Java unit testing tool competition. It describes the infrastructure used, including modifications to work with libraries not in DEFECT4J and a new tool to detect flaky tests. 69 classes across 8 projects were used as benchmarks. The results showed EvoSuite performed best overall based on generated test coverage, effectiveness on real and mutated code, and test quality metrics. Statistical analysis confirmed EvoSuite was better than the other 3 participating tools. Lessons included benefits of statistical analysis and selecting non-trivial test classes.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
The document discusses software metrics and how they can be used to drive testing projects. It defines key metrics like effort variation, schedule variation, and productivity that can help plan projects, control processes, and identify areas for improvement. Data collection strategies and operational definitions for various metrics are also provided.
The document discusses software metrics and how they can be used to drive testing projects. It defines key metrics like effort variation, schedule variation, and productivity that can help plan projects, control processes, and identify areas for improvement. Data collection strategies and operational definitions for metrics are also provided to help institutionalize a metrics program.
The document discusses software metrics and how they can be used to measure and improve software testing projects. It defines key metrics like effort variation, schedule variation, and productivity that can help plan projects, control processes, and identify areas for improvement. The document also provides details on how to operationalize these metrics by specifying data collection procedures, responsibilities, and sources. Institutionalizing a metrics program is important to drive continuous process enhancement.
Let the CI spot the holes in tested code with the Descartes toolOscar Luis Vera Pérez
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IRJET- Deep Learning Model to Predict Hardware PerformanceIRJET Journal
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Top 12 Most Useful AngularJS Development Tools to Use in 2025GrapesTech Solutions
AngularJS remains a popular JavaScript-based front-end framework that continues to power dynamic web applications even in 2025. Despite the rise of newer frameworks, AngularJS has maintained a solid community base and extensive use, especially in legacy systems and scalable enterprise applications. To make the most of its capabilities, developers rely on a range of AngularJS development tools that simplify coding, debugging, testing, and performance optimization.
If you’re working on AngularJS projects or offering AngularJS development services, equipping yourself with the right tools can drastically improve your development speed and code quality. Let’s explore the top 12 AngularJS tools you should know in 2025.
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Java Architecture
Java follows a unique architecture that enables the "Write Once, Run Anywhere" capability. It is a robust, secure, and platform-independent programming language. Below are the major components of Java Architecture:
1. Java Source Code
Java programs are written using .java files.
These files contain human-readable source code.
2. Java Compiler (javac)
Converts .java files into .class files containing bytecode.
Bytecode is a platform-independent, intermediate representation of your code.
3. Java Virtual Machine (JVM)
Reads the bytecode and converts it into machine code specific to the host machine.
It performs memory management, garbage collection, and handles execution.
4. Java Runtime Environment (JRE)
Provides the environment required to run Java applications.
It includes JVM + Java libraries + runtime components.
5. Java Development Kit (JDK)
Includes the JRE and development tools like the compiler, debugger, etc.
Required for developing Java applications.
Key Features of JVM
Performs just-in-time (JIT) compilation.
Manages memory and threads.
Handles garbage collection.
JVM is platform-dependent, but Java bytecode is platform-independent.
Java Classes and Objects
What is a Class?
A class is a blueprint for creating objects.
It defines properties (fields) and behaviors (methods).
Think of a class as a template.
What is an Object?
An object is a real-world entity created from a class.
It has state and behavior.
Real-life analogy: Class = Blueprint, Object = Actual House
Class Methods and Instances
Class Method (Static Method)
Belongs to the class.
Declared using the static keyword.
Accessed without creating an object.
Instance Method
Belongs to an object.
Can access instance variables.
Inheritance in Java
What is Inheritance?
Allows a class to inherit properties and methods of another class.
Promotes code reuse and hierarchical classification.
Types of Inheritance in Java:
1. Single Inheritance
One subclass inherits from one superclass.
2. Multilevel Inheritance
A subclass inherits from another subclass.
3. Hierarchical Inheritance
Multiple classes inherit from one superclass.
Java does not support multiple inheritance using classes to avoid ambiguity.
Polymorphism in Java
What is Polymorphism?
One method behaves differently based on the context.
Types:
Compile-time Polymorphism (Method Overloading)
Runtime Polymorphism (Method Overriding)
Method Overloading
Same method name, different parameters.
Method Overriding
Subclass redefines the method of the superclass.
Enables dynamic method dispatch.
Interface in Java
What is an Interface?
A collection of abstract methods.
Defines what a class must do, not how.
Helps achieve multiple inheritance.
Features:
All methods are abstract (until Java 8+).
A class can implement multiple interfaces.
Interface defines a contract between unrelated classes.
Abstract Class in Java
What is an Abstract Class?
A class that cannot be instantiated.
Used to provide base functionality and enforce
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Adobe Media Encoder is a transcoding and rendering application that is used for converting media files between different formats and for compressing video files. It works in conjunction with other Adobe applications like Premiere Pro, After Effects, and Audition.
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Standalone and Integrated:
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SAC 2018: "AutoPUT: An Automated Technique for Retrofitting Closed Unit Tests into Parameterized Unit Tests"
1. AutoPUT: An Automated Technique for Retrofitting
Closed Unit Tests into Parameterized Unit Tests
Keita Tsukamoto†, Yuta Maezawa†,
Shinichi Honiden‡
April 12, 2018 1
† Udzuki Inc., Japan
‡ Waseda University, Japan
The 33rd ACM/SIGAPP Symposium On Applied Computing
Pau, France
2. Outline
• Background
– Test Suite Maintenance
– Parameterized Unit Test
• Challenges: Fully Automated Parameterizing
• Approach: AutoPUT
– Detecting CUTs to be Parameterized
– Generating PUTs from detected CUTs
• Evaluation
• Conclusion
April 12, 2018 2
3. Background – Test Suite Maintenance
April 12, 2018 3
• Test suites are not static entities[1]
– Developers must keep maintaining test suites
• Adding new tests, modifying existing tests, or delete
unnecessary tests
Test suite maintenance is necessary to build reliable software
Old
Test Suite
New
Tests
Developer
add
New
Test Suite
Ex. adding
new tests
Old
Test Suite
Developer
modify
New
Test Suite
Ex. modifying
existing tests
Existing
Tests
Modified
Tests
[1] L. Pinto et al. “Understanding Myths and Realities of Test-Suite Evolution”, FSE 2012
4. Background – Test Maintainability
April 12, 2018 4
• Developers need to understand purpose of tests[2]
– Good readability helps developers to maintain test suites[3]
– Simplifying tests increases understandability of tests[4]
Test maintainability ≒ Test understandability
Test Suite
maintain
read Developers
New
Test Suite
Understandability ↑
Test Suite
maintain
read Developers
New
Test Suite
Understanding ↑ Understanding ↓
Understandability ↓
[2] E. Daka et al. “Modeling Readability to Improve Unit Tests”, FSE 2015
[3] B. Zhang et al. “Towards Automatically Generating Descriptive Names for Unit Tests”, ASE 2016
[4] Y. Lei et al. “Minimization of Randomized Unit Test Cases”, ISSRE 2005
5. Background – Parameterized Unit Test (PUT)
April 12, 2018 5
Unit tests allowing parameters•
Enable to separate parameters and procedures–
Less code and independent of concrete parameters•
Easier to understand than closed unit tests (CUTs)[5]
However, PUTs are rare in the real world projects
[5] G. Fraser et al. “Generating Parameterized Unit Tests”, ISSTA 2011
// no parameters -> CUT
@Test public void testAdd1() {
int expected = 3;
int actual = Adder.add(1, 2);
assertEquals(expected, actual);
}
@Test public void testAdd2() {
int expected = 0;
int actual = Adder.add(1, -1);
assertEquals(expected, actual);
}
// PUT
@Theory public void testAdd(int[] params) {
int expected = params[0];
int actual =
Adder.add(params[1], params[2]);
assertEquals(expected, actual);
}
parameterize
6. Related Work – Parameterized Unit Tests
April 12, 2018 6
• Generating parameters for PUTs by symbolic execution[6]
Not full solution for practicality problem
PUTs
prepare Unit
Meister
input generate
Developers
Parameters
[6] N. Tillman et al. “Parameterized Unit Tests”, FSE 2005
High coverage
• Parameterizing auto-generated CUTs[5]
EvoStuite
generate
CUTs
parameterized
unit tests
input parameterize
PUTs
Less code than CUTs
Retrofitting existing unit tests is more difficult[5]
• Semi-automated retrofitting CUTs into PUTs[7]
CUTs
read
Pex
promote
parameters retrofit
PUTs
Semi
PUTs
input
Developers
[7] S. Thummalapenta et al. “Retrofitting Unit Tests for Parameterized Unit Testing”, FASE 2011
Who
detects?
7. Challenges – Fully Automated Retrofitting CUTs
April 12, 2018 7
Developers do not have
sufficient resources to retrofit CUTs
2 challenges for fully automated retrofitting existing CUTs
CUTsCUTs
CUTsCUTs
About 25% of unit tests are easy
to be parameterized[8]
Project #CUT #PUT
Codec 643 24
Compress 867 11
Math 4100 1
But...
[8] D. Saff et al. “Theories in practice: Easy-to-write specifications that catch bugs”, MIT CSAIL Technical Report 2008
Fully automated retrofitting can solve practicality problem
Project
Repository
CUTs to be
parameterized
1. detect 2. generate
PUTs
check
Developers Common procedure and different parameters
8. Approach – Automated Technique
for Retrofitting CUTs into PUTs (AutoPUT)
April 12, 2018 8
• Addressing two challenges
– Detecting CUTs to be parameterized → Detector
– Generating PUTs from detected CUTs → Generator
AutoPUT can help developers maintain test suites
Developers
1. Detector 2. Generator
@Test
Project
Repository
No
AutoPUT
Degraded?
Coverage
Measuring
@Theory
@DataPoints
@Theory
@DataPoints
9. Approach – Detecting CUTs to be Parameterized
April 12, 2018 9
• Using *AST-based code-clone detection technique
– CUTs to be parameterized = kind of code clones[8]
– Effective detecting by static program analysis
• Ignoring values of literal nodes, and considering names of
identifiers
Accurately detecting in practical amount of time
*AST = Abstract Syntax Tree
add
compare
add
different procedure
@Test
public void testAdd1 () {
int expected = 3;
int actual = Adder.add(2, 1);
assertEquals(expected, actual);
}
@Test
public void testAdd2() {
int expected = 1;
int actual = Adder.add(-1, 2);
assertEquals(expected, actual);
}
@Test
public void testSub1() {
int expected = 1;
int actual = Subber.sub(2, 1);
assertEquals(expected, actual);
}
sub
AST
10. Approach – Generating PUTs from detected CUTs
April 12, 2018 10
• Extracting parameters from AST of each CUT
– Parameter nodes = literal nodes, or constant value nodes
• Parameter nodes with inconsistent values in all CUTs → parameters
• Replacing parameter nodes with abstract variables
– Judge each parameter is related to expected outputs or not
Automated generating PUTs in practical amount of time
input expected
Related to expected outputs
→ replaced with `expected`
proceduresparameters
abstract variable
node
CUTA
CUTB
11. Approach – Requirements for Generated PUTs
April 12, 2018 11
• Retrofitting CUTs often induce degradation[7]
Guaranteeing that degradation does not occur
CUTs
retrofit
PUTs
× Compile Error
× Test Failure
× Decrease in Coverage
• Confirming whether degradation does not occur
– If degradation occurs, AutoPUT excludes the PUT
Generated
PUTs
success? decreases?
Original
CUTs
no
Coverage
Measuring
&
Comparison
yes
yes
Compile
&
Test Execution
PUTs
No degradation
Discard Discard
12. Evaluation - Experimental Setting
• Subjects
April 12, 2018 12
Project # of Class # of Test Class # of Existing CUTs # of Existing PUTs
BCEL 378 63 112 1
Codec 63 56 643 24
Compress 195 127 867 11
CSV 11 19 286 2
Digester 169 103 218 2
FileUpload 839 19 66 8
Math 847 522 4100 1
• Research Questions
1. Can AutoPUT accurately detect CUTs to be
parameterized in a practical amount of time?
2. Can AutoPUT effectively generate PUTs in a
feasible amount of time?
13. Evaluation - Results and Discussions (RQ1)
April 12, 2018 13
• Results
– SCC[9]: the-state-of-the-art tool for detecting code-clones
AutoPUT scored higher or the same score than SCC
in a practical amount of time
AutoPUT SCC4 SCC5 SCC6
Precision Recall Precision Recall Precision Recall Precision Recall
Avg. Score 72.5% 55.2% 1.3% 61.5% 2.4% 55.2% 3.8% 44.0%
Total Time 9.3 sec. N/A N/A N/A
[9] H. Sajnani et al. “SourcererCC: Scaling code clone detection to big-code.”,ICSE 2016
• Discussions
– Higher precision score than SCC* and the same recall
score as SCC5
– Spent 9.3 sec. for 7 projects → sufficiently practical
14. Evaluation - Results and Discussions (RQ2)
April 12, 2018 14
• Results
AutoPUT generated 204 PUTs, reducing the
number of statements by 64.0%, in 8.5 hours
# of Generated PUTs # of Converted CUTs Reduction Ratio Time
204 675 64.0% 8.5 hrs
– Reduction ratio[5]: to
– Time: including coverage measuring time
#statements of PUTs #statements of CUT s
• Discussions
– 204 generated PUTs: 95.3% of PUT candidates
• 4.7% inducing degradation: implementation issue (e.g., Enum-typed
parameters)
– 64.0% reduction of statements that developers must maintain
– Spent 8.5 hours for all PUTs → sufficiently feasible (in a day)
15. Conclusion
April 12, 2018 15
• Background
– PUT: easier to understand than CUT
• Challenge
– Fully automated CUT-PUT retrofitting
• Approach: AutoPUT
– Detecting CUTs with similar structure
– Identifying parameters and generate PUTs
• Experimental Results
– Generated 204 PUTs in 8.5 Hours
AutoPUT can help developers to maintain test suites
16. Future Work – Extending AutoPUT
April 12, 2018 16
• Investigating meaningfulness of generated PUTs
– Current: Reducing #CUTs
– Future: To be accepted by actual developers
• Retrofitting PUTs to CUTs
– Current: Unkind error messages from PUTs
– Future: To handle software failures easily
• Combining with symbolic execution
– Current: Guaranteeing no degradation
– Future: To achieve higher coverage
Plan to make AutoPUT more practical and applicable