Detailed cocomo

Duration: 2 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 project estimation models, presented by an instructor from Knowledge Gate. It begins with an introduction to the Detailed COCOMO model, which is described as the most comprehensive and accurate model for estimating software projects. The model's definition is provided, highlighting that it divides a project into multiple components or modules and accounts for interactions between cost drivers and project phases. The lecture explains that the model estimates effort and duration for each component using the Intermediate COCOMO model and then sums these up. It also notes that the Detailed COCOMO model considers factors such as software reuse, hardware constraints, and personnel/team attributes. The presentation then transitions to a slide titled 'Other Empirical models,' which lists several formulas for estimating effort (E) based on the number of lines of code (KLOC). These include the Watson & Felix Model (E = 5.2 x KLOC^0.91), the Bailey-Basili Model (E = 5.2 + 0.73 x KLOC^1.16), the Simple Boehm Model (E = 3.2 x KLOC^1.05), and the Doty Model (E = 5.288 x KLOC^1.047). The video concludes with a 'Thanks for watching' screen.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video starts with a title card for 'SOFTWARE ENGINEERING' and '#knowledgegate'. It then transitions to a lecture slide titled 'Detailed COCOMO'. The instructor, Sanchit Jain, explains the definition of the Detailed COCOMO model, which is described as the most comprehensive and accurate model that divides a software project into multiple components or modules and accounts for interactions between cost drivers and project phases. The slide lists that the model estimates effort and duration for each component using the Intermediate COCOMO model and then sums them up, and it considers software reuse, hardware constraints, and personnel/team attributes. The presentation then moves to a slide titled 'Other Empirical models', which displays several formulas for effort estimation: E = 5.2 x KLOC^0.91 (Watson & Felix Model), E = 5.2 + 0.73 x KLOC^1.16 (Bailey-Basili Model), E = 3.2 x KLOC^1.05 (Simple Boehm Model), and E = 5.288 x KLOC^1.047 (Doty Model). The video ends with a 'Thanks for watching' screen.

The lecture provides a structured overview of software estimation techniques, starting with the most detailed model, Detailed COCOMO, which emphasizes a component-based approach and considers multiple project factors. It then contrasts this with a set of simpler, empirical models that use a direct mathematical relationship between effort and lines of code. This progression highlights the difference between a comprehensive, multi-factor model and more straightforward, formulaic approaches, giving students a comparative understanding of estimation methodologies.

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