Decomposition Based Estimation

Duration: 3 min

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Inside: a video lesson and guided study material.

Module outline

  1. English Comprehension/Verbal Ability: Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions), Vocabulary, Sentence Correction (Error Correction), Sentence Completion (Fill in the blanks), Sentence Re-arrangements (Para Jumbles) (Narrative Sequencing), Subject Verb Agreement (Verb Noun Agreement), Tenses, Verb, Articles, Preposition, Conjunction, Pronoun, Noun, Adjectives, Adverb, Modals, Cloze Test (Cloze Passage), Active Passive Voice (Voice), Direct and Indirect Speech, Sentence Construction
  2. Logical Ability: Coding Decoding, Directions (Direction Test), Analogy, Inequality, Data Sufficiency, Series (Number and Letter Series) (Numerical Relations and Reasoning), Syllogisms, Seating Arrangements, Puzzle Solving (Puzzles), Blood Relations, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Ranking, Order and Sequence, Logical Sequence Of Words, Data Arrangement, Venn Diagram, Calendar, Clock
  3. Quantitative Ability: Logarithm, Permutation and Combination, Probability, Profit and Loss, Speed Time and Distance, Mixture and Alligation, Partnership, Simple Interest and Compound Interest, Percentage, Ratio and Proportion (Ratios), Average, Time and Work, Pipe and Cistern, Number System, Divisibility Rules, HCF LCM, Algebra, Powers and Exponents (Surds and Indices), Mensuration and Geometry, Age Problems, Data Interpretation, Sequence and Series (Series)
  4. Basic Analytical Ability: Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Analytical Reasoning (Counting Figures Reasoning), Cubes & Dices, Selection Decision Table (Decision Making)
  5. Computer Programming: Pseudo Codes, Coding Questions by Yash Sir, C Programming by Yash Sir, Programming in C++, Java, Python for Placements by Yash Sir
  6. DBMS (Computer Science): Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Transaction Management, Concurrency Control, Database Security & Authorization
  7. SQL (Computer Science): SQL
  8. OS (Computer Science): Introduction to OS, Process Management, CPU Scheduling, Deadlock, Process Synchronization, Threads & Process Creation, Memory Management, Virtual Memory, File Management
  9. Computer Networks (Computer Science): 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, Hardware basics, Network Security
  10. Data Structures (Computer Science): Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing
  11. OOPs (Computer Science): Object Oriented Programming (OOPS)
  12. Software Engineering (Computer Science): Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management
  13. SVAR Conversational - Spoken English: Communication Assessment
  14. HR Interview: MR & HR Interview Preparation
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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