Overview

Although there are countless books on statistics, few are dedicated to the application of statistical methods to software engineering. Simple Statistical Methods for Software Engineering: Data and Patterns fills that void. Instead of delving into overly complex statistics, the book details simpler solutions that are just as effective and connect with the intuition of problem solvers. Sharing valuable insights into software engineering problems and solutions, the book not only explains the required statistical methods, but also provides many examples, review questions, and case studies that provide the understanding required to apply those methods to real-world problems. After reading this book, practitioners will possess the confidence and understanding to solve day-to-day problems in quality, measurement, performance, and benchmarking. By following the examples and case studies, students will be better prepared able to achieve seamless transition from academic study to industry practices. * Includes boxed stories, case studies, and illustrations that demonstrate the nuances behind proper application * Supplies historical anecdotes and traces statistical methods to inventors and gurus * Applies basic statistical laws in their simplest forms to resolve engineering problems * Provides simple techniques for addressing the issues software engineers face The book starts off by reviewing the essential facts about data. Next, it supplies a detailed review and summary of metrics, including development, maintenance, test, and agile metrics. The third section covers the fundamental laws of probability and statistics and the final section presents special data patterns in the form of tailed mathematical distributions. In addition to selecting simpler and more flexible tools, the authors have also simplified several standard techniques to provide you with the set of intellectual tools all software engineers and managers require.

Full Product Details

Author:   C. Ravindranath Pandian ,  Murali Kumar
Publisher:   Taylor & Francis Ebooks
Imprint:   Auerbach
ISBN:  

9781439816622

ISBN 10:   143981662
Pages:   388
Publication Date:   25 September 2013
Audience:   General/trade ,  College/higher education ,  General ,  Tertiary & Higher Education
Format:   Electronic book text
Publisher's Status:   Active
Availability:   Available To Order  
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Table of Contents

DATA Data, Data Quality, and Descriptive Statistics The Challenge That Persists Bringing Data to the Table Requires Motivation Data Quality Visual Summary Numerical Descriptive Statistics (Numerical Summary of Data) Case Study: Interpretation of Effort Variance Descriptive Statistics Application Notes Concluding Remarks Definition of Descriptive Statistics References Suggested Readings Truth and Central Tendency Mean Median Mode Geometric Mean Harmonic Mean Interconnected Estimates Weighted Mean Robust Means Two Categories Truth Application Notes Case Study: Shifting the Mean References Suggested Reading Data Dispersion Range-Based Empirical Representation Dispersion as Deviation from Center Skewness and Kurtosis Coefficient of Dispersion Application Contexts In a Nutshell Case Study: Dispersion Analysis of Data Sample Reference Suggested Readings Tukey's Box Plot: Exploratory Analysis The Structure of the Box Plot Customer Satisfaction Data Analysis Using the Box Plot Tailoring the Box Plot Applications of Box Plot Core Benefits of Box Plot Twin Box Plot Case Study 1: Business Perspectives from CSAT Box Plots Case Study 2: Process Perspectives from CSAT Box Plots References Deriving Metrics Creating Meaning in Data Deriving Metrics as a Key Performance Indicator Estimation and Metrics Paradigms for Metrics GQM Paradigm Difficulties with Applying GQM to Designing a Metrics System Need-Driven Metrics Meaning of Metrics: Interpreting Metric Data Our Categories of Metrics Business Metrics Project Metrics Process Metrics Subprocess Metrics Product Metrics Case Study: Power of Definitions References Suggested Readings Achieving Excellence in Software Development Using Metrics Examples of Project Metrics Examples of Product Metrics Examples of Process Metrics Subprocess Metrics Converting Metrics into Business Information Case Study: Early Size Measurements Project Progress Using Earned Value Metrics References Suggested Readings Maintenance Metrics Fusion of Frameworks in Software Maintenance Metric-Based Dashboards References Suggested Readings Software Test Metrics Project Metrics Process Metrics Product Metrics Testing Size: Test Case Point Risk Metric Predicting Quality Metrics for Test Automation Case Study: Defect Age Data References Suggested Readings Agile Metrics Classic Metrics: Unpopular Science Two Sides of Classic Metrics Metrics for Agile: Humanization The Price of Humanization Common Agile Metrics Adding More Agile Metrics Case Study: Earned Value Management in the Agile World References Suggested Reading LAWS OF PROBABILITY Pattern Extraction Using Histogram Choosing the Number of Intervals Process Signature Uniqueness of Histogram Signature Histogram Shapes Mixture Process Capability Histogram Histogram as a Judge From One Point to One Histogram Case Study: Goal Entitlement Creating a Histogram Interpretation References The Law of Large Numbers Life Is a Random Variable Plots of Probability Distribution A Comparison of Application of the Four Distributions Bayes Theorem References Suggested Reading Law of Rare Events Exponential Distribution Poisson Distribution Bathtub Curve of Reliability: A Universal Model of Rare Events Nonhomogenous Poisson Process Goel-Okumoto NHPP Model Different Applications of GO Model References Suggested Readings Grand Social Law: The Bell Curve First-Order Approximation of Variation Estimation Error Viewing Requirement Volatility Risk Measurement Combining Normal PDFs: The Law of Quadrature An Inverse Problem Process Capability Indices z Score Calculation Sigma Level: Safety Margin Statistical Tests References Suggested Readings Law of Goal Compliance: Uniform Distribution Bounded Distribution Random Number Generators Shuttle Time Parkinson's Law Censored Process Perfect Departure Estimating Calibration Uncertainty with Minimal Information References Suggested Readings Law for Estimation: Triangular Distribution Bell Curve Morphs into a Triangle Mental Model for Estimation Mean Median Other Statistics Skew Three-Point Schedule Estimation Beta Option Triangular Risk Estimation Parameter Extraction References Pareto Distribution-The Law of Life: 80/20 Aphorism Structure of Pareto An Example The 80/20 Law: Vital Few and Trivial Many Generalized Pareto Distribution Duane's Model Tailing a Body References TAILED DISTRIBUTIONS Software Size Growth: Log-Normal Distribution Log-Normal Processes Building a Log-Normal PDF for Software Design Complexity Working with a Pictorial Approach Features Addition in Software Enhancement A Log-Normal PDF for Change Requests From Pareto to Log-Normal Some Properties of Log-Normal Distribution Case Study-Analysis of Failure Interval References Gamma Distribution: Making Use of Minimal Data Gamma Curves for Clarification Time Data Shifting the Gamma PDF Generating Clarification Time Scenarios with Gamma PDF Built from Minimal Data NIST Formula for Gamma Parameter Extraction Applying Gamma Distribution to Software Reliability Growth Modeling References Weibull Distribution: A Tool for Engineers Weibull Curves Parameter Extraction Standard Weibull Curve Three-Parameter Weibull Software Reliability Studies Putnam's Rayleigh Curve for Software Reliability Cost Model Defect Detection by Reviews New Trend Rayleigh Model-Success and Failure References Gumbel Distribution for Extreme Values A Science of Outliers Gumbel Minimum PDF Gumbel Parameter Extraction-A Simple Approach Gumbel Minimum: Analyzing Low CSAT Scores Gumbel Maximum: Complexity Analysis Minima Maxima Comparisons Analyzing Extreme Problems References Gompertz Software Reliability Growth Model Gompertzian S Curves Modeling Reliability Growth with Gompertzian S Curves Building a Reliability Growth Curve Gompertz Software Reliability Growth Model Curves Predicting With the Gompertz Model More Attempts on Gompertzian Software Growth Reliability Model How to Implement Gompertz Model in Software Testing Gompertz Curve versus GO NHPP Model References Suggested Readings

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Author Information

C.R. Pandian has authored multiple books. He researches quantitative methods in software project management. Research interests also include risk discovery and management. He is a frequent speaker and holds seminars on these topics. Murali Kumar SK has more than 20 years of multi-disciplinary cross-functional experience in multiple industries that include metallurgical, mechanical, electronics, and IT/software environments. He has held leadership positions in implementing multiple quality standards and processes for various business units and business domains.

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