[justify]Applying Six Sigma Methodology In Software Engineering
Six sigma is a rigorous and disciplined methodology that uses data and statistical analysis to measure and improve a company's operational performance by identifying and eliminating "defects" in manufacturing and service-related processes. Commonly defined as 3.4 defects per million opportunities, Six Sigma can be defined and understood at three distinct levels namely metric, methodology and philosophy . The fundamental objective of the Six Sigma methodology is the implementation of a measurement-based strategy that focuses on process improvement and variation reduction through the application of Six Sigma improvement projects (statistical tools). This is accomplished through the use of two Six Sigma sub-methodologies: DMAIC (Define, Measure, Analyze, Improve and Control) and DMADV (Define, Measure, Analyze, Design and Verify). Also what makes Six Sigma such an important software engineering methodology is its outside-in customer centric approach to resolving problems. In this paper, all the overall concepts of Six Sigma have been introduced. Also it describes about the features and benefits of Six Sigma over other software quality models that are used for process optimization in Software Industry.
Six Sigma is a rigor for measuring and controlling quality in any process. One of the quotes on Six Sigma has been cited below by Mikel Harry as ;“If we can’t express what we know in the form of numbers, we really don’t know much about it. If we don’t know much about it, we can’t control it. If we can’t control it, we are at the mercy of chance.”
Mikel J. Harry
President & CEO
Six Sigma Academy, Inc.
Before delving further into this topic lets start by understanding what exactly six sigma means to a layman.“Quantitatively, Six Sigma means that the average process generates 3.4 defects per million.” – from “6 Sigma Quality – The Goal and The Journey”Where Sigma is a symbol meaning how much deviation exists in a set of data - sometimes called a bell curve, or a standard normal distribution. In a standard normal distribution, 50% of the values lie above the mean (average) and 50% of the values lie below. In Statistics we take it a step further and delineate certain data points within that timeline. Thus the Six Sigma process is a quantitative process. It is based on measurement.
Hence put in a better way Six Sigma also means that one can measure any process in terms of the variation or deviation from expected agreed upon results.
There are two types of variation: Common Causes and Special Causes
Since anything we do varies, even if only slightly, from the plan. Since no result can exactly match our intention, we usually think in terms of ranges of acceptability for whatever we plan to do. Those ranges of acceptability (or tolerance limits) respond to the intended use of the product of our labors–the needs and expectations of the customer.
For any process with a standard distribution (something that looks like a bell-shaped curve), the probability is 68.26% that the next value will be within one standard deviation from the mean. The probability is 95.44% that the same next value will fall within two standard deviations. The probability is 99.73% that it will be within three sigma; and 99.994% that it will be within four sigma .
The surprising discovery of companies which initially developed Six Sigma, or world-class, quality is that the best quality does not cost more. It actually costs less. The reason for this is something called cost-of-quality. Cost-of-quality is actually the cost of deviating from quality–paying for things like rework, scrap and warranty claims. Making things right the first time, even if it takes more effort to get to that level of performance–actually costs much less than creating then finding and fixing defects.
Understanding Key terms of Six Sigma
Six Sigma: A process capability in which variability is reduced so that 6 standard deviations fit between the process mean and the specification limits.
Process: A process is a collection of activities that takes one or more kinds of input and creates output that is of value to the customer.
CTQ: CTQ is Critical to Quality. A product feature or process step that must be controlled to guarantee that you deliver what the customer wants.
Defect (D): Any non-conformities in a process or product.
Unit (N): A process step where each unit must be observable and countable with a definite starting and stopping point
Opportunity (O): An opportunity is a product or process characteristic that adds or subtracts value from the product. To be quantifiable, each opportunity must be independent. Thus opportunity is event which can be measured that provides a chance of not meeting a customer requirement.
Normal Distribution: A bell-shaped curve showing a frequency distribution which often occurs in nature.
Mean: The average of measured data.
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