Kompleksitas Algoritma
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This document discusses algorithm complexity. It defines algorithm complexity as a way to compare algorithms by how much time and space they require based on the size of the input, ignoring implementation details. There are two types of complexity: time complexity T(n) and space complexity S(n). Time complexity looks at the worst case, best case, and average case time requirements. Space complexity measures the memory used based on the input size. Understanding complexity is important for determining an algorithm's efficiency and how its requirements increase with larger inputs.
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This document discusses the complexity of algorithms and the tradeoff between time and space complexity. It defines algorithm complexity as how execution time increases with input size. Different algorithms may complete the same task with varying time/space requirements. The complexity of bubble sort and linear search algorithms are analyzed as examples. The concept of space-time tradeoffs is introduced, where using more space can reduce time complexity and vice versa. Genetic algorithms are proposed as an efficient method to solve large-scale construction time-cost tradeoff problems.
2. Introduction to Algorithm.pptx
This document introduces algorithms and their basics. It defines an algorithm as a step-by-step procedure to solve a problem and get the desired output. Algorithms can be implemented in different programming languages. Common algorithm categories include search, sort, insert, update, and delete operations on data structures. An algorithm must be unambiguous, have well-defined inputs and outputs, terminate in a finite number of steps, and be feasible with available resources. The document also discusses how to write algorithms, analyze their complexity, and commonly used asymptotic notations like Big-O, Omega, and Theta.
Design and Analysis of Algorithms.pptx
This document discusses algorithms and their analysis. It defines an algorithm as a finite sequence of unambiguous instructions that terminate in a finite amount of time. It discusses areas of study like algorithm design techniques, analysis of time and space complexity, testing and validation. Common algorithm complexities like constant, logarithmic, linear, quadratic and exponential are explained. Performance analysis techniques like asymptotic analysis and amortized analysis using aggregate analysis, accounting method and potential method are also summarized.
Fundamentals of the Analysis of Algorithm Efficiency
This document discusses analyzing the efficiency of algorithms. It introduces the framework for analyzing algorithms in terms of time and space complexity. Time complexity indicates how fast an algorithm runs, while space complexity measures the memory required. The document outlines steps for analyzing algorithms, including measuring input size, determining the basic operations, calculating frequency counts of operations, and expressing efficiency in Big O notation order of growth. Worst-case, best-case, and average-case time complexities are also discussed.
Introduction to data structures and Algorithm
This document provides an introduction to data structures and algorithms. It discusses key concepts like algorithms, abstract data types (ADTs), data structures, time complexity, and space complexity. It describes common data structures like stacks, queues, linked lists, trees, and graphs. It also covers different ways to classify data structures, the process for selecting an appropriate data structure, and how abstract data types encapsulate both data and functions. The document aims to explain fundamental concepts related to organizing and manipulating data efficiently.
Cupdf.com introduction to-data-structures-and-algorithm
This document provides an introduction to data structures and algorithms. It discusses key concepts like abstract data types (ADTs), different types of data structures including linear and non-linear structures, analyzing algorithms to assess efficiency, and selecting appropriate data structures based on required operations and resource constraints. The document also covers topics like classifying data structures, properties of algorithms, analyzing time and space complexity, and examples of iterative and recursive algorithms and their complexity analysis.
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Software Testing Documentation
The document discusses the importance of documentation in software testing. It notes that documentation is needed to record test implementation and results, and helps direct testing and reuse tests. There are different types of test documentation, including test plans, specifications, and analysis reports. Effective documentation provides benefits like training, communication, maintenance, and historical reference. Test documentation should be maintained throughout the software development life cycle.
Software Productivity Measurement
1) The document discusses various metrics for measuring software productivity, quality, and testing at different stages of the software development process. It covers metrics for requirements, design, source code, testing, maintenance, and processes.
2) Key metrics discussed include defects per KLOC, function points, defect removal efficiency, and size-oriented vs function-oriented metrics. Metrics for object-oriented design, user interfaces, and project management are also summarized.
3) Collecting measures and developing metrics at each stage of development allows indicators to be obtained and improvements to be made to both the product and process. A variety of productivity, quality, and testing metrics can provide meaningful insights.
Debugging (Part 2)
The document discusses various debugging techniques. It describes the brute force method, which involves taking memory dumps and adding print statements to locate bugs. This generates a large amount of data but requires little thought. The cause elimination method deduces or induces potential causes and tests hypotheses to eliminate them one by one. Other methods include trial and error, backtracking code to find failures, and forward tracking to see when results first became wrong. Principles for locating errors include thinking carefully and getting a fresh perspective. Principles for repairing errors are to fix the underlying cause, check for new errors, and re-evaluate the design when making changes.
Debugging
The document discusses debugging processes and techniques. It defines debugging as the process of finding, correcting and removing bugs from programs. There are three main types of errors: syntactic, semantic, and logic errors. The debugging process involves reproducing the problem reliably, finding the source of the error, fixing just that one error, testing the fix, and optionally looking for more errors. Key debugging techniques include inserting print statements, using a debugger, explaining the code to someone else, and fixing only one error at a time. The overall goal of debugging is to methodically match symptoms to causes to locate and correct errors in code.
Software Testing Strategy (Part 2)
This document discusses various types of software testing strategies including integration testing, continuous integration testing, and system testing. It provides details on techniques like top-down integration, bottom-up integration, smoke testing, regression testing, interface testing, and acceptance and installation testing. The objectives are to learn about integration testing, continuous integration testing, and system testing. Key aspects of different strategies like top-down vs bottom-up integration and categories of system test cases are compared. Determining when to stop testing is noted as one of the most difficult questions.
Software Testing Strategy
The document provides an overview of software testing strategies. It discusses that the main objective of software testing is to systematically find errors without taking much time. It emphasizes the importance of choosing the best testing strategy. It then covers various software testing strategies including unit testing, integration testing, validation testing, and system testing. Unit testing involves testing individual software components in isolation while integration testing focuses on testing the interaction between integrated components. The document contrasts the incremental and "big bang" approaches to integration testing and argues that the incremental approach is more effective.
Object Oriented Testing
The document discusses object-oriented testing strategies. It explains that in object-oriented testing, the component being tested is a class-object rather than a function. Unit testing focuses on testing each class's operations and attributes. Integration testing focuses on testing groups of collaborating classes. Validation testing is based on use case scenarios from the object-oriented analysis model. The document provides details on techniques for unit testing, integration testing, and validation testing of object-oriented systems.
Testing Technique (Part 2)
The document discusses various software testing techniques, including blackbox and whitebox testing. Blackbox testing focuses on functional requirements without seeing the internal structure and includes equivalence class testing, limit testing, robustness testing, and requirements testing. Whitebox testing uses internal program structure to derive test cases and focuses on all logical paths and decisions. It includes basis path testing and control structure testing such as conditional and loop testing. The document provides examples of applying these techniques.
Testing Technique
This document discusses various software testing techniques classified into static, dynamic, and exhaustive categories. Static techniques like verification and static analysis do not require executing the program. Dynamic techniques execute the program and are divided into structure-oriented techniques like control flow testing and function-oriented techniques like function coverage and use case testing. Exhaustive techniques aim to cover all possible test cases. The document provides examples and explanations of different testing methods.
Testing Plan
The document discusses software testing concepts including:
1. It defines key terms related to software defects such as errors, defects, failures, and faults.
2. It outlines the different phases of software testing from component/unit testing to acceptance testing and discusses principles of good testability.
3. It provides guidance on writing test plans and cases, including reviewing requirements, identifying test suites, and transforming use cases into test cases.
Methodology Selection Strategy
This document discusses methodology selection strategy for systems analysis and design. It explains that there is no single framework appropriate for every software project, and the methodology needs to be adapted to the specific team and product. It outlines important factors to consider like clarity of requirements, technology familiarity, system complexity, schedule constraints. The methodology should guide the team's process but allow flexibility. The success of the methodology is measured by timely delivery of quality product increments that satisfy stakeholders.
Software Testing - Software Quality (Part 2)
The document discusses software quality and testing. It defines software reliability as the probability of failure-free operation of a computer program in a given environment and time. Testing aims to find errors in programs to improve reliability, rather than show a program works correctly. The document outlines ISO quality standards for software development including ISO 9001, which provides a framework for quality processes and procedures. Organizations must comply with ISO 9001 requirements to achieve quality certification.
Software Testing - Software Quality
The document discusses software quality assurance and testing. It defines software quality as having two aspects - quality of design which includes requirements and specifications, and quality of conformance which focuses on implementation. Software Quality Assurance (SQA) includes quality management, effective engineering processes, formal techniques, testing strategies, documentation control, and measurement/reporting. SQA aims to ensure requirements quality, design quality, code quality, and effective quality control. Non-functional attributes like reliability, usability, and performance largely determine a software's subjective quality from a user's perspective.
Computer Evolution and Performance
ajeng.savitri@teknokrat.ac.id
https://teknokrat.ac.id/en/
https://spada.teknokrat.ac.id/
Software Testing - Introduction
The document describes an introduction to software testing presentation. It discusses the software development life cycle (SDLC) which includes planning, analysis, design, implementation, and maintenance phases. It then explains different software development methodologies like structured design, rapid application development, and agile development. Finally, it provides definitions and goals of software testing, and presents a model of the software testing process.
Sequence Diagram
Dokumen ini membahas tentang analisis dan desain sistem untuk sistem ATM. Terdapat penjelasan mengenai diagram urutan dan diagram kelas yang menggambarkan interaksi antar objek dalam sistem ATM untuk beberapa kasus penggunaan seperti mengecek saldo, mentransfer uang, dan mengambil uang. Dokumen ini juga menampilkan contoh desain antarmuka pengguna untuk sistem ATM menggunakan perangkat lunak Netbeans.
Activity Diagram
Dokumen ini membahas tentang pemodelan proses bisnis dengan diagram aktivitas dan menggunakan sistem ATM sebagai contoh studi kasus. Diagram aktivitas digunakan untuk memvisualisasikan aliran kerja dalam suatu proses bisnis yang melibatkan beberapa departemen. Contoh aktivitas yang dimodelkan meliputi memasukkan PIN, mengecek saldo, mentransfer uang, mengambil uang, dan logout.
Use Case Diagram
The document discusses use case diagrams for systems analysis and design. It provides examples of use case diagrams for an appointment system and an ATM system. Key elements of use case diagrams are described, including actors, use cases, relationships like generalization, include and extend, and the system boundary. The ATM system example demonstrates how a use case diagram can model the interactions between an actor (user) and the various functions of an ATM machine like checking balance, transferring money, withdrawing money and logging out.
Requirement Gathering
Systems analysis and design techniques are discussed, including requirement gathering and business process analysis. Requirement gathering involves document analysis, interviews, questionnaires, and observation to understand user needs. Functional requirements define system functions while non-functional requirements address system qualities. Business process analysis assesses current processes and can involve automation, improvement, or reengineering. Barriers to effective requirement gathering include users finding additional needs after seeing a system ("yes but" syndrome), incomplete discovery of all needs ("undiscovered ruins" syndrome), and communication gaps between users and developers.
Business Value
The document discusses a feasibility analysis for a proposed CD Selections Internet Order System project.
The technical feasibility is determined to be medium risk overall, as the application and technology are somewhat unfamiliar but compatible with existing systems. The economic feasibility analysis shows a 229% return on investment over 3 years with a break-even point of 1.7 years. The organizational feasibility is low risk as the project aligns with goals of increasing sales and becoming more Internet-savvy.
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原版一模一样【微信：741003700 】【(sdsu毕业证书)圣地亚哥州立大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
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如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
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留信网认证的作用:
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
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5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
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校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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This session delves into how these challenges were addressed through:
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3. Implement task groups in Release pipeline stages to facilitate the execution of tests, ensuring consistency and efficiency across deployment phases.
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Programming design and development represents a vital step in keeping pace with technological advancements and meeting ever-changing market needs. This course is intended for anyone who wants to understand the fundamental importance of software development and design, whether you are a beginner or a professional seeking to update your knowledge.
Course objectives:
1. **Learn about the basics of software development:
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- Identify the role of programmers and designers in software projects.
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- Discussing common design patterns such as Object-Oriented Design.
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Kompleksitas Algoritma
- 1. Ajeng Savitri P, M.Kom Analysis & Strategy of Algorithm Pertemuan 2
- 3. OBJECTIVE To learn the definition of algorithm complexity To learn types of algorithm complexity
- 4. COMPLEXITY OF ALGORITHM • A problem may have many solving algorithm. • An algorithm must not only correct, but also efficient. • Efficiency of algorithm is measured by how much time and space memory required to run it. • An efficient algorithm can minimizes the time and space requirements. • The need for time and space an algorithm depends on the size of the input (n), which show the amount of data processed.
- 5. “Algorithm complexity is something designed to compare two algorithms at the idea level - ignoring low-level details such as the implementation programming language, the hardware the algorithm runs on, or the instruction set of the given CPU” “Complexity is also a tool that allows us to explain how an algorithm behaves as the input grows larger”
- 6. Cont. . . • Quantity that used to explain an abstract model of measurement of time / space is the complexity of algorithms. • There are two kinds of algorithm complexity: – The complexity of time T(n) – The complexity of space S(n) • Using the scale complexity of the time/space algorithm, we can determine the rate of increase time (space) required by algorithms with increasing input size n • Abstract model of measurement of time / space should be independent of any machine and compiler consideration.
- 7. Why Do We Need An Efficient Algorithm ? 105 15 20 25 30 35 40 Ukuran masukan 10 102 103 104 105 1 1 detik 1 menit 1 jam 1 hari Waktukomputasi(dalamdetik) 10-1 10-4 x 2n 10-6 x n3 10-6 x 2n 10-4 x n3
- 8. TIME COMPLEXITY • But the computation time requirement as above is less acceptable: – In practice we do not have real time information on how to performa particular operation – A computer with a different architecture would be different durations for each type of operation. • In addition, the program execution time is also influenced by the programming language compiler used.
- 9. Types Of Time Complexity • Tmax (n): the time complexity for the worst case, the maximum time requirement. • Tmin (n): the time complexity for the best case, the minimum time requirement. • Tavg (n): the time complexity for the average case, the average time requirement
- 10. Space Complexity • The complexity of space S(n) : measured by memory used to structure of data is contained in the algorithm as a function of the input size n