Software Quality Control Interview Preparation Guide

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Input variables or signal factors are those that can be set at the desired levels by the experimenter.
That is, these variables can be controlled during the experiment and at the design stage and or in the actual production stage.

Experimental runs under identical conditions should be replicated a suffi-cient number of times to obtain accurate estimates of experimental errors and the effects of the input variables on the response variables

American Society for Quality Control

BETA Distribution has four parameters: a and b, which are, respectively, the minimum and maximum values that a beta random variable can assume, and g and h, which are the shape parameters.

The beta distribution is a more flexible probability density function compared to the normal distribution because it can accommodate different ranges (not always from α to + α , as in the normal distribution) and different shapes from left skew

The Basic forms of variation are Assignable variations and Coomon Variations

In Process Improvement, we define the customer as the next person in line whoever receives the output or value of the product or service.

It process the output to the customer service by the agent regarding the product or output of the severices.

Quality Center has the same functions as test-director it is the difference of the Lay-out or appearence can also be called as cosmetic difference

T.D:- Test cases to be written on word/excel

Q.C :- directly write test cases

Quality Center is the updated version of a Test Director, with more speed.There is not much of a difference between test-director and quality center

product quality means we concentrate always final quality but in case of process quality we set the process parameter

QA people roles will be do the auditing to make sure that in testing, everything has happened as expected (proper documents created, have been they tracked in exact time, are all the documents stored in share path?....etc) so that they are making sure about the process which delivers the quality products.

The Three Measures of Common use in Quality Control is Mean, Median and Mode
The three commonly used measures in quality control are Mean (also called as Average), Median (is the value that divides a series of ordered observations so that the number of items above it equals to the number below it) and Mode (is the value that occurs with the highest frequency).

The maintenance phase is considered to be the blackout phase. Maintenance is the last stage of the software development life cycle. After the product has been released, the maintenance phase keeps the software up to date with environment changes and changing user requirements. It is called the blackout phase because the system is not accessible and is said to "black out" during maintenance.

Here's a simplified response:

1) Begin by learning about the products and services; gain domain knowledge
2) Understand workflow and processes throughout the product life cycles
3) Determine where defects are caused in these workflows and processes
4) Establish checkpoints at these junctures
5) Establish reliable methods of defect detection
6) Establish quality standards and targets for quality improvement
7) Tie Quality Control effort to organization's cost and profitability
8) Report findings

Lower specification limit (LSL)

Also known as the lower control limit (LCL).

A) Meaning 99.999997% perfect; only 3.4 defects in a million.

The answer to this question depends on its context. If you are referring to the Software Development Life Cycle (SDLC), then the answer still depends on the model you are referring to. The number and nature of phases varies greatly between software development models. Generally, the phases of software development are:

Phase 1: Planning (a.k.a. the Requirements Phase)
Phase 2: Design
Phase 3: Implementation
Phase 4: Verification
Phase 5: Maintenance

different type of metrics captured
Defect Density- no of defects par KLOC
Defect Severity distribution- which measured in term of defect severity
Defect Removal Effectiveness- total number of defects prior to delivery
Review Effectiveness

Accepted practice uses a width of three standard deviations around the population mean ( 3) to establish the control limits for control chart.Anything that falls between the range of ( 3) is teh common cause of variation & anything that falls out of the range of ( 3) is special cause of variation

DRR = # of Defects Rejected/Tester to assess the tester performance

DRR = # of Defects Rejected/module to assess the module complexity

DRR = # of Defects Rejected/Team/Project to assess the team performance.

and

DLR = # of Defects uncovered/Tester

DLR = # of Defects uncovered/Module

DLR = # of Defects uncovered/Project

( Uncovered- Defects missed out by the testing team)

What is the cost the business would have to incur if a big error was missed if these steps were not followed properly? Damages in terms of money (cost of litigation, regulatore claims, cost of fixing those bugs) and to the brand name of the company (open to discussion).
For me, that is the cost of Quality

The answer to this question depends on what it is you are attempting to control. For example, it is much easier to assess and control quality in a manufacturing facility than it is to assure quality control in the software industry.

Quality Control in the software industry refers to the art and practice of testing. Any test effort that strives to identify and report defects in order to improve products and services is practicing Quality Control.

So how can we assure Quality Control? By establishing test processes that prove to deliver products and services that customers require at a level of quality that they demand. And who are these customers? Your company and clients, of course.

the p-chart is a type of control chart used to monitor the proportion of nonconforming units in a sample, where the sample proportion nonconforming is defined as the ratio of the number of nonconforming units to the sample size

D) Binomial

The design of experiments may be used to help improve the capability of a process by identifying the process and product variables that affect the mean and the variance of the quality characteristic(s) of the product.