Science-based (Software) Test Planning - Fewer Tests, Better Coverage

Why Should You Attend:

According to industry benchmark data an average project that costs in the area of $1,000,000 will require something like 4,000 test cases. Roughly 20% of those cases are duplications of other tests – they reveal nothing new about the quality of the application.

Using the method discussed in this session you can expect to greatly reduce the number of redundant test cases, ensure important cases are not missed, and reduce the total number of tests required, and reduce total testing costs by 40%. We will also discuss case studies conducted by a major outsourcing firm that have shown the number of defects found per tester hour increased 2.4 times.

The method discussed in this session applies to all types of software testing.

Areas Covered in the Seminar:

• Background: “Design of Experiments”.
• Software Testing as an “Experiment”.
• What Are “Combinatorial” Interactions ? Why Do They Matter?
• The Business Case for Combinatorial Testing.
• Case Study: Amazon Checkout.
• Limitation and Caveats.
• References.

Who Will Benefit:

This webinar will provide value to anyone involved in testing software – both software specialist and business domain specialist involved in testing software. A technical background is not required.  Those that would benefit most include:

• Application Domain Specialist
• User Acceptance Test Participants
• Business Analysts
• Requirement Engineers
• Project managers
• Quality assurance managers
• Process improvement specialists
• Software metrics specialists
• Software process specialists

Instructor Profile:

Gary Gack, is the founder and President of Process-Fusion.net, a provider of Assessments, Strategy advice, Training, and Coaching relating to integration and deployment of software and IT best practices. Mr. Gack holds an MBA from the Wharton School, is a Lean Six Sigma Black Belt and an ASQ Certified Software Quality Engineer. He has more than 40 years of diverse experience, including more than 20 years focused on process improvement. He is the author of many articles and a book entitled Managing the Black Hole: The Executive's Guide to Software Project Risk.

Topic Background:

Testing software is expensive and often not especially effective. When properly analyzed, something that is rarely done, we often find very substantial levels of redundancy among test cases. We also often find important test cases have been missed entirely. Testing software always accounts for at least 30% of total software cost, and sometimes as much as 70%.

In terms of calendar time, testing often requires 40% of the total schedule and sometimes much more. Both customers and developers are constantly wondering “how much is enough?” According to industry benchmark data an average project that costs in the area of $1,000,000 will require something like 4,000 test cases. Roughly 20% of those cases are duplications of other tests – they reveal nothing new about the quality of the application. Even with 4,000 test cases a high quality software product will deliver around 200 defects (95% pre-release discover rate). More typical average projects will deliver 400 – 800 defects (60-80% discovery rate).

A single test types (e.g., unit, integration, system, acceptance, etc.) can be expected to find between 30% and 50% of the defects present at the time the test is conducted. A sequence of 5 to 8 different test types is the normal case.

Certain industry areas, notably pharmaceuticals, have long used a statistical method known as “Design of Experiments” to design test regimes that gain maximum information from a relatively minimal number of tests. More recently this approach has been adapted to the field of software testing. There are many different kinds of software testing including some, such as unit and integration testing that require significant technical knowledge. Other types of testing, such as system level and user acceptance, rely much more heavily on business and domain knowledge. The method discussed in this session applies to all types of software testing.

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