Decomposition Based Estimation

Duration: 3 min

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Inside: a video lesson and guided study material.

Module outline

  1. HPSC Interview Preparation: Interview Preparation
  2. Python Language: 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
  3. Data Structure & Algorithms: Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Algorithm Analysis, Sorting Algorithms, Minimum Spanning Trees, Time Complexity Analysis, Graph Theory, Shortest Path Algos
  4. Computer Networks: Introduction to CN, Data Communication, DLL: Access Control, DLL: Flow Control, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Security
  5. Computer System & Organisation: Basics of COA, Cache Memory Organization, Input Output Organisation, Pipelining, Instr Formats & Modes, Control Unit Design, Programming the Basic Computer
  6. Database Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, SQL, File Organization & Indexing, Transaction Management, Concurrency Control
  7. Operating Systems: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Deadlock, Threads & Process Creation, Memory Management, Virtual Memory, Disc Scheduling
  8. Digital Logic & Circuits: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation
  9. Theory Of Computation: Introduction to TOC, Deterministic FA (DFA), Non-Deterministic FA, Regular Expressions, Grammar, Regular Language Properties, Pushdown Automata & CFG, Turing Machines, Moore & Mealy Machines
  10. Compiler Design: Intro to Compilers, Lexical Analysis, Grammar & CFG, Syntax Analysis: Top-Down, Syntax Analysis: Bottom-Up, Semantic Analysis & SDT, Intermediate Code Gen, Code Optimization, Run Time Environment
  11. Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, Data Warehousing & Mining
  12. Introduction to Emerging Trends: Emerging Tech Trends
  13. Digital Society & Cyber Law: Society, Law & Ethics
  14. HPSC PGT Previous Year Paper:
  15. HPSC Assistant Professor Previous Year Paper:
  16. Live Sessions: HPSC Live Sessions, EMRS TGT/PGT Live Class
  17. Full Mock Test:
  18. HPSC PGT - MOCK Test Series:
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video is an educational lecture on software engineering estimation techniques, presented by an instructor from Knowledge Gate. The first segment introduces 'Decomposition based estimation' as a method for large projects, explaining that it involves breaking down a problem into smaller parts. It details two primary approaches: 'Direct Estimation (White Box)', which uses size-oriented metrics like KLOC (thousands of lines of code), and 'Indirect Estimation (Black Box)', which uses function-oriented metrics like FP (function points). The second segment transitions to a set of fundamental formulas for project estimation, including Effort = Size / Productivity, Cost = Effort X Pay, and Duration = Effort / Team Size. The instructor uses a whiteboard to write and explain these equations, which are displayed on the screen. The video concludes with a 'Thanks for Watching' screen.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video opens with a title card for 'SOFTWARE ENGINEERING' and '#knowledgegate'. It then transitions to a slide titled 'Decomposition based estimation'. The instructor explains that this method is used for large projects by decomposing them into smaller problems. The slide lists two estimation bases: 'Direct Estimation (White Box): Size oriented metrics (KLOC)' and 'Indirect Estimation (Black Box): Function oriented metrics (FP)'. The instructor, visible in a small window, elaborates on these concepts, with the on-screen text clearly defining the terms and their acronyms.

  2. 2:00 – 2:47 02:00-02:47

    The slide changes to display a list of formulas for project estimation. The instructor explains the relationships between key project variables. The formulas shown are: Effort = Size / Productivity, Productivity = Size / Effort, Size = Effort X Productivity, Cost = Effort X Pay, Duration = Effort / Team Size, Team Size = Effort / Duration, and Effort = Duration X Team Size. The instructor writes these equations on the whiteboard, and the text is clearly visible on the screen. The video ends with a 'THANKS FOR WATCHING' screen.

The lecture progresses from a high-level introduction to software estimation methods to the specific, quantitative formulas used to calculate project effort, cost, and duration. It first establishes the conceptual framework of decomposition-based estimation, distinguishing between direct (size-based) and indirect (function-based) methods. It then provides the core mathematical relationships that allow a project manager to derive one variable from others, forming the basis of a practical estimation process. The flow moves from theory to application.

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