Skip to main content

Betting in Testing



“I’ve completed my testing of this feature,

and I think it's ready to ship”


“Are you willing to bet on that?”


No, Don't worry, I’m not going to list various ways you could test the feature better or things you might have forgotten.

Instead, I recommend you to ask yourself that question next time you believe you are finished. 

Why? It might cause you to analyse your belief more critically. We arrive at a decision usually by means of a mixture of emotion, convention and reason. Considering the question of whether the feature and the app are good enough as a bet is likely to make you use a more evidence-based approach.

Image result for gambling
Testing is gambling with your time to find information about the app.

Why do I think I am done here? Would I bet money/reputation on it? I have a checklist stuck to one of my screens, that I read and contemplate when I get to this point. When you have considered the options, you may decide to check some more things or ship the app. Either could be the right decision.

Then the app fails…


The next day you log on and find that the feature is broken. It turns out the programmer and you had missed the bug.

Firstly, before you beat yourself up, you may have made the right decision. Even though the feature was broken - it may have been appropriate to test the way you did.

Or it may be that you need to update your checklist, skills, or automated tests etc.

How could I fail to catch the bug and yet have made the right decisions?
  1. You and the rest of the team made choices on the best information you had available. You stopped and thought about what was the right thing to do. Based on that decision you shipped.
  2. As you could not test all the behaviours/permutations that the app was subject to, you made an educated guess based on the data available. You used an approach that suited the system as you knew it, and you would make the same decision again.

Assuming that you made the wrong choice because it had the wrong outcome is called Resulting. It is similar to the hindsight bias and can change our future views and behaviour. 

Example:


If the chance of the app failing was 0.1% (in reality we can’t usually place % on these things, but for the sake of argument...) then we might have been able to ship 1000 times and likely only seen that sort of bug once. If that’s the sort of risk profile our Product Owner is happy with, then we made the right call.

It could have been a greater risk to the business to not have that feature deployed. (Think regulatory deadlines, rival product launches etc) 

In summary, every time you test, you are gambling with your time. You can use your knowledge and expertise to help make the right bets. Sometimes those bets don’t pay off.

You may miss a bug because you need to update your checklists, skills, knowledge or automated tests. Or it could be that you made a judgement call that is right 99.99% of the time, but just not that time and your test approach was correct.

Thinking this way can help focus your work on productive and valuable behaviours. For example, We won’t panic and write too many automated tests, slowing down our team’s delivery schedule. Or we may decide to examine the impact of the code change failing, rather than just looking at IF it is failing. Will it just affect a low priority system? Or will it have a catastrophic effect?

Comments

  1. It has to be said, though, that if your company is putting the responsibility on you as a tester to declare a product "ready to ship", then they are loading you with a responsibility that you should not have. Given that no software is ever guaranteed to be 100% bug-free, there is always the likelihood that an unforeseen problem will emerge after shipping; under this scenario, the company will blame you for it when it was not your fault. Putting pressure on a tester to declare a product ready to ship is almost a self-fulfilling prophecy, given that you are being treated as a gatekeeper and presumably being put under pressure to make the earliest possible release date.

    Signing off a release is a business decision that needs to be taken by a product owner or other senior person who is of sufficient status in the company to take that decision. As a tester, the best you can do is to advise the PO that you have been unable to find obvious major defects in the time available. The decision to ship is way above your pay grade (if you are not in the actual management chain itself), and if your company still believes these things, then you have some way to go over actually teaching your company what testing is about.

    ReplyDelete
  2. Your essay is fantastic. Here, you've given very insightful knowledge. fun casino Cheshire The information you have provided us with is genuinely beneficial and significant. Thank you for providing this information.

    ReplyDelete

Post a Comment

Popular posts from this blog

XSS and Open Redirect on Telegraph.co.uk Authentication pages

I recently found a couple of security issues with the Telegraph.co.uk website. The site contained an Open redirect as well as an XSS vulnerability. These issues were in the authentication section of the website, https://auth.telegraph.co.uk/ . The flaws could provide an easy means to phish customer details and passwords from unsuspecting users. I informed the telegraph's technical management, as part of a responsible disclosure process. The telegraph management forwarded the issue report and thanked me the same day. (12th May 2014) The fix went live between the 11th and 14th of July, 2 months after the issue was reported. The details: The code served via auth.telegraph.co.uk appeared to have 2 vulnerabilities, an open redirect and a reflected Cross Site Scripting (XSS) vulnerability. Both types of vulnerabilty are in the OWASP Top 10 and can be used to manipulate and phish users of a website. As well has potentially hijack a user's session. Compromised URLs, that exp

What possible use could Gen AI be to me? (Part 1)

There’s a great scene in the Simpsons where the Monorail salesman comes to town and everyone (except Lisa of course) is quickly entranced by Monorail fever… He has an answer for every question and guess what? The Monorail will solve all the problems… somehow. The hype around Generative AI can seem a bit like that, and like Monorail-guy the sales-guy’s assure you Gen AI will solve all your problems - but can be pretty vague on the “how” part of the answer. So I’m going to provide a few short guides into how Generative (& other forms of AI) Artificial Intelligence can help you and your team. I’ll pitch the technical level differently for each one, and we’ll start with something fairly not technical: Custom Chatbots. ChatBots these days have evolved from the crude web sales tools of ten years ago, designed to hoover up leads for the sales team. They can now provide informative answers to questions based on documents or websites. If we take the most famous: Chat GPT 4. If we ignore the