M.E. Computer Science & Engineering:Software Metrics

Thapar University
In Patiala

Price on request
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Important information

  • Master
  • Patiala

Important information

Where and when

Starts Location
On request
Thapar University P.O Box 32, 147004, Punjab, India
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Course programme

Semester I

Advanced Data Structures
Software Design and Construction
Research Methodology
Software Engineering Concepts and Methodologies
Advanced Computer Architecture

Semester II

Parallel and Distributed Computing
Advanced Database Systems
Soft Computing Neural Networks
Embedded Systems

Semester III

Thesis (Starts)

Semester IV

Thesis (Continued)

Software Metrics

Basics of measurement: Measurement in everyday life, measurement in software engineering, scope of software metrics, representational theory of measurement, measurement and models, measurement scales, meaningfulness in measurement, goal-based framework for software measurement, classifying software measures, determining what to measure, software measurement validation, empirical investigation, types of investigation, planning and conducting investigations.

Software-metrics data collection and analysis: What is good data, how to define the data, how to collect the data, how to store and extract data, analyzing software-measurement data, frequency distributions, various statistical techniques.

Measuring internal product attributes: Measuring size, aspects of software size, length, functionality and complexity, measuring structure, types of structural measures, control-flow structure, modularity and information flow attributes, data structures.

Measuring external product attributes: Modeling software quality, measuring aspects of software quality, software reliability, basics of software reliability, software reliability problem, parametric reliability growth models, predictive accuracy, recalibration of software-reliability growth predictions, importance of operational environment, wider aspects of software reliability.

Metrics for object-oriented systems: The intent of object-oriented metrics, distinguishing characteristics of object-oriented metrics, various object-oriented metric suites � LK suite, CK suite and MOOD metrics.

Metrics for component-based systems: The intent of component-based metrics, distinguishing characteristics of component-based metrics, various component-based metrics.

Resource measurement: Measuring productivity, teams, tools, and methods.

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