Phy-gital Roundtable: Breakfast Roundup from Germany and Netherlands

02 May '15 | Debjyoti Paul

German Shoppers: Meet Them in the Fast Lane to Phy-gital

15 January '15 | Ralf Reich

Shoppers Will Share Personal Information (But They Don’t Want to be “Friends”)

15 January '15 | Anil Venkat

Modernize or Perish: Property and Casualty Insurers and IT Solutions

14 January '15 | Manesh Rajendran

Benelux Reaches the Phy-gital Tipping Point: Omnichannel Readiness is Crucial

13 January '15 | Anil Gandharve

The New Omnichannel Dynamic: Finding Core Principles Across Industries

13 January '15 | Debjyoti Paul

Technology does not disrupt business – CIO day 2014 Roundup

02 December '14 | Anshuman Singh

Apple Pay – The Best Is Yet To Come

02 December '14 | Indy Sawhney

Digital transformation is a business transformation enabled by technology

01 December '14 | Amit Varma

3 Stages of FATCA Testing and Quality Assurance

06 October '14 | Raman Suprajarama

3 Reasons why Apple Pay could dominate the payments space

18 September '14 | Gaurav Johri

Beacon of Hope: Serving Growth and Customer Satisfaction

05 August '14 | Debjyoti Paul

The Dos and Don’ts of Emerging Technologies Like iBeacon

30 July '14 | Debjyoti Paul

What You Sold Us On – eCommerce Award Finalist Selections

17 July '14 | Anshuman Singh

3 Steps to Getting Started with Microsoft Azure Cloud Services

04 June '14 | Koushik Ramani

8 Steps to Building a Successful Self Service Portal

03 June '14 | Giridhar LV

Innovation outsourced – a myth or a mirage or a truth staring at us?

13 January '14 | Ramesh Hosahalli

What does a mobile user want?

03 January '14 | Gopikrishna Aravindan

Data and Knowledge in Testing

Posted on: 18 March '09

A central concept of software testing – perhaps this is the rare case of a principle in software testing that is at once necessary and sufficient – is that its ultimate purpose is to gather the data needed to verify that all release criteria for the software under test (SUT) have been met.

All of the test engineers I know or work with though understand that data by itself is not knowledge. We establish quality objectives that support business objectives for the SUT. How will the pencil be used? Have other solutions been tried? We design tests that will affirm or deny predictive or descriptive propositions such as the “API GetLastRegName functions as designed”; or we compare our test data with target, baseline, or benchmark data, and conclude that the SUT has exceeded or fallen short of what we sought or expected.

As we accumulate more relevant data (where relevance is in terms of the selected quality objectives), we believe that we have increased our knowledge about the SUT. Oddly, we also tend to think that as our knowledge increases, our ignorance decreases. I don’t think so. Ignorance persists; or as a software designer friend of mine might say, we persist ignorance across multiple domains.

The fault lies in thinking that knowledge is a zero sum game. I certainly don’t remember thinking that, and I very much doubt that you do either. But it’s is in our minds, perhaps a fossil surviving from when such thinking was necessary for survival. If knowledge of our SUT did have an empirical limit, then the complement of our knowledge would be ignorance – our ignorance would decrease as our knowledge increased. We could one day conceivably eliminate our ignorance of the SUT – and with it, eliminate all bugs and all risk. But there are always other environments in which to test, different processors, different chipsets.

Unless we limit knowledge’s domain, it cannot push back the frontiers of ignorance. We structure our knowledge by the form of our inquiry. Certain inquiries have very limited domains, offering hope (and therefore the illusion) of mastery. But without curiosity and appreciation, which are both forms of inquiry, we learn nothing.

Mindtree Blog Archives

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