Empirical Models

Duration: 5 min

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Module outline

  1. About the course: About the Course
  2. Mathematical Aptitude: Number System, HCF LCM, Divisibility Rules, Percentage, Ratio and Proportion (Ratios), Average, Profit and Loss, Time and Work, Mensuration and Geometry, Simple Interest and Compound Interest, Speed Time and Distance, Age Problems, Mixture and Alligation, Algebra, Powers and Exponents (Surds and Indices), Pipe and Cistern, Trigonometry, Statistics, Data Interpretation, Arithmetic
  3. Logical Reasoning: Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Analogy, Blood Relations, Directions (Direction Test), Ranking, Order and Sequence, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Seating Arrangements, Syllogisms, Venn Diagram, Puzzle Solving (Puzzles), Logical Sequence Of Words, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Analytical Reasoning (Counting Figures Reasoning), Cubes & Dices, Calendar, Clock
  4. General Knowledge: Physics, Chemistry, Biology, History, Geography, Polity, Indian Economy, GK- Current Affairs, Static General Knowledge
  5. Art of Teaching: Nature, Objectives & Characteristics of Teaching, Learners & Learning Process, Factors Affecting Teaching, Methods of Teaching, Classroom Management & Communication, Teaching-Learning Aids & ICT Integration, Evaluation, Assessment & Measurement, Professional Development & Ethics, Policies, Acts & Contemporary Issues, Curriculum & School Environment, Guidance, Counselling & Mental Health, Inclusive Education & Special Needs, Micro-Teaching & Teaching Skills, Lesson Planning
  6. Environmental Science: Environment
  7. Unit 1 | Digital Electronics: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation
  8. Unit 2 | COA: Basics of COA, Floating Point Rep, Cache Memory Organization, Input Output Organisation, Instr Formats & Modes, Control Unit Design, Register Transfer and Microoperations, Programming the Basic Computer
  9. Unit 3 | C Programming: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling
  10. Unit 3 | Data Structures: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  11. Unit 4 | Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  12. Unit 5 | Operating System: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, Disc Scheduling, File Management
  13. Unit 6 | Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, File Organization & Indexing, Relational Algebra, SQL, Transaction Management, Concurrency Control
  14. Unit 7 | Computer Networks: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, DLL: Error Control, DLL: Framing, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Net Layer:Routing Protocol, Transport Layer Services, TL: Congestion & UDP, Application Layer
  15. Unit 8 | Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  16. Unit 9 | Object Oriented Programming: OOPS with C++
  17. Unit 10 | Web Development: HTML Basics, CSS, JavaScript-Basics, XML
  18. Unit 11 | Theory Of Computation: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Moore & Mealy Machines, Pushdown Automata & CFG, Turing Machines
  19. Unit 12 | IoT: Emerging Tech Trends, Cloud Computing
  20. Unit 13 | Artificial Intelligence: Approaches to AI, Search Algorithms, Game Playing, Knowledge Representation, Natural Language Processing, Artificial Neural Networks, Genetic Algorithms
  21. Unit 14 | E-Commerce: Electronic Data Interchange and E-Commerce, Role of Internet and Web Services, Security in Mobile and Wireless Computing, Physical Security and Cryptography Basics, Cyber Safety and Security
  22. Unit 15 | Multimedia: Multimedia
  23. Unit 16 | Python: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis, DSA using Python
  24. Full Length Mock Tests — General Paper:
  25. Full Length Mock Tests — Computer Science (Paper II):
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This educational video provides a lecture on software estimation models, specifically focusing on empirical models and the COCOMO (Construction Cost Model) framework. The presentation begins by defining empirical models as estimation techniques that use formulas derived from historical data, based on metrics like Lines of Code (LOC) or Function Points (FP). It then introduces the COCOMO model, developed by Barry Boehm in 1981, as the most widely used and universally accepted regression-based estimation technique. The lecture details the three primary software classes defined by COCOMO: Organic (application), Semidetached (utility), and Embedded (system), explaining that classification is based on product and team characteristics. The video concludes by presenting a worked example, writing the formula 'E = 100 pm' on the screen, which represents the effort (E) in person-months for a project, illustrating the application of the model.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video opens with a title card for 'SOFTWARE ENGINEERING' from 'Knowledge Gate'. The lecture begins by defining 'Empirical models' as estimation models that use empirically derived formulas for predicting based on LOC or FP. The instructor explains that different authors provide different mathematical formulas for these models. The slide states that out of all COCOMO models, the most versatile and universally accepted one is being discussed. The instructor, visible in a small window, begins to explain the concept, and the text 'E = 100 pm' is written on the screen, representing a formula for effort estimation.

  2. 2:00 – 4:49 02:00-04:49

    The lecture transitions to the COCOMO (1981) model, which is described as the most widely used estimating technique. The instructor explains it is a regression-based model developed by Barry Boehm, which postulates three important classes of software. The slide lists these as Organic (application), Semidetached (utility), and Embedded (system). The instructor elaborates on the classification criteria, stating that product characteristics and development team characteristics are considered. Examples are given: data processing and scientific programs are applications, while compilers and linkers are utilities. The slide also mentions that the relative complexity of these three categories is in the ratio 1:3:9 for application, utility, and system programs, respectively. The instructor continues to explain the model's application, with the 'E = 100 pm' formula remaining visible on the screen as a key example.

The video presents a structured lesson on software cost estimation, progressing from the general concept of empirical models to the specific, widely-accepted COCOMO framework. It establishes the foundational idea that estimation relies on formulas derived from past data, then narrows the focus to COCOMO as the premier model. The core of the lesson is the classification of software into three distinct types—Organic, Semidetached, and Embedded—based on their complexity and development context. This classification is crucial for applying the COCOMO model correctly, as it dictates the parameters used in the estimation formula, which is exemplified by the 'E = 100 pm' equation. The synthesis of these concepts provides a clear methodology for estimating software project effort.

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