Empirical Models

Duration: 5 min

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Inside: a video lesson and guided study material.

Module outline

  1. Paper 1 | Reasoning Test: Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Data Sufficiency, Syllogisms, Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Seating Arrangements, Blood Relations, Puzzle Solving (Puzzles), Venn Diagram, Analogy, Ranking, Order and Sequence, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Selection Decision Table (Decision Making), Logical Sequence Of Words, Cubes & Dices, Analytical Reasoning (Counting Figures Reasoning), Calendar, Alphabet and Word Tests, Numerical Reasoning, Number System, Averages, Mixtures and Alligation, Percentages, Series Completion, Analogy and Classification, Patterns in Two-Dimensional Figures, Coding and Decoding, Mathematical Operations, Direction Sense, Algebra, Logical Deduction and Induction, Syllogisms and Venn Diagrams, Critical Reasoning, Order, Ranking and Sequence, Reading Comprehension, Profit, Loss and Discount, Constraint Puzzles, Speed, Time and Distance, Traverse Surveying, Ratio and Proportion, Time and Work, Mensuration, Permutations and Combinations, Clocks and Calendars, Data Interpretation, Assembling and Grouping of Shapes, Transformation of Shapes, Three-Dimensional Patterns and Visualization, Cubes and Dice, Vocabulary, Blood Relations, Data Sufficiency
  2. Paper 1 | Numerical Analysis: Number System, HCF LCM, Percentage, Ratio and Proportion (Ratios), Average, Sequence and Series (Series), Powers and Exponents (Surds and Indices), Algebra, Statistics, Data Interpretation, Mental Ability, Arithmetic, Logarithm, Profit and Loss, Simple Interest and Compound Interest, Speed Time and Distance, Mixture and Alligation, Mensuration and Geometry, Permutation and Combination, Probability, Sequences and Series, Powers, Surds and Logarithms, Subject-Verb Agreement, Sentence Completion, Geometry, Sentence Structure and Correction, Data Sufficiency, Trigonometry, Measurement and Quantity Take-Off, Signal Space and Optimum Detection, Probability, Statistics, Algebra, Time and Work, Speed, Time and Distance, Data Interpretation, Graph Theory, Combinatorics
  3. Paper 1 | General Knowledge: State Wise GK, History, Geography, GK- Current Affairs, Static General Knowledge, Polity, Indian Economy
  4. Paper 1 | DBMS: 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, Relational Calculus, Database Security & Authorization, Database Recovery
  5. Paper 1 | Data Communication & Network: 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, Network Security, Security in Mobile and Wireless Computing, Input Output Organisation, File Management, Hardware Basics, Terms & Abbreviations, Protocols & Services, Network Access Technologies, Cyber Safety and Security, Basics of COA
  6. Paper 2 | System Analysis & Design: Fundamentals of Systems and Information Systems, SDLC and Development Approaches, Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Documentation and Software Requirements Specification (SRS), SDD and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, Security, Risk Management & Maintenance, extras
  7. Paper 2 | Programming Language (Java): Java Fundamentals, OOPS with Java, Array, String, IO & References, Generic & Collections, Multithreading, Java, Miscellaneous, Control Flow, OOPS with C++, Stack, C Fundamentals, Arrays & Pointers, Object Oriented Technology, Control Flow & Looping, Introduction to Python
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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