Category Archives: Testing

Lessons learned from writing a ten-part blog series

After leaving Quest back in August 2020, I spent some time working on ideas for a new venture. During this time, I learned some useful lessons from courses by Pat Flynn and got some excellent ideas from Teachable‘s Share What You Know Summit. When I launched my new software testing consultancy, Dr Lee Consulting, I decided to try out one of the ideas I’d heard for generating content around my new brand and so started a blog series, inspired most notably by Terry Rice.

After committing to a ten-part series of posts, I decided to announce my intention publicly (on Twitter and LinkedIn) to keep myself honest, but chose not to commit to a cadence for publishing the parts. I felt that publishing a new blog post once a week was about right and made an internal note to aim for this cadence. Some posts took longer to write than others and the review cycle was more involved for some posts. The series also spread over the Christmas/New Year period, but the entire series took me just on three months to complete so my cadence ended up being close to what I initially thought it would be.

My blogging over the last several years has usually been inspired by something I’ve read or observed or an event I’ve attended such a conference or meetup. These somewhat more spontaneous and sporadic content ideas mean that my posts have been inconsistent in both topic and cadence, not that I see any of this as being an issue.

Committing to a series of posts for which the subject matter was determined for me (in this case by search engine data) meant that I didn’t need to be creative in coming up with ideas for posts, but instead could focus on trying to add something new to the conversation in terms of answering these common questions. I found it difficult to add much nuance in answering some of the questions, but others afforded more lengthy and perhaps controversial responses. Hopefully the series in its entirety is of some value anyway.

My thanks again to Paul Seaman and Ky for reviewing every part of this blog series, as well as to all those who’ve amplified the posts in this series via their blogs, newsletters, lists and social media posts.

The ten parts of my first blog series can be accessed using the links below:

  1. Why is software testing important?,
  2. How does software testing impact software quality?
  3. When should software testing activities start?
  4. How is software testing done?
  5. Can you automate software testing?
  6. Is software testing easy?
  7. Is software testing a good career?
  8. Can I learn software testing on my own?
  9. Which software testing certification is the best?
  10. What will software testing look like in 2021?

(Feel free to send me ideas for any topics you’d like to see covered in a multi-part blog series in the future.)

Donation of proceeds from sales of “An Exploration of Testers” book

In October 2020, I published my first software testing book, “An Exploration of Testers”. As I mentioned then, one of my intentions with this project was to generate some funds to give back to the testing community (with 100% of all proceeds I receive from book sales being returned to the community).

I’m delighted to announce that I’ve now made my first donation as a result of sales so far, based on royalties for the book in LeanPub to date:

LeanPub royalties

(Note that there is up to a 45-day lag between book sales and my receipt of those funds, so some recent sales are not included in this first donation amount.)

I’ve personally rounded up the royalties paid so far (US$230.93) to form a donation of US$250 (and covered their processing fees) to the Association for Software Testing for use in their excellent Grant Program. I’m sure these funds will help meetup and peer conference organizers greatly in the future.

I will make further donations of royalties received from book sales not covered by this first donation.

“An Exploration of Testers” is available for purchase via LeanPub and a second edition featuring more contributions from great testers around the world should be coming soon. My thanks to all of the contributors so far for making the book a reality and also to those who’ve purchased a copy, without whom this valuable donation to the AST wouldn’t have been possible.

Common search engine questions about testing #10: “What will software testing look like in 2021?”

This is the final part of a ten-part blog series in which I’ve answered some of the most common questions asked about software testing, according to search engine autocomplete results (thanks to Answer The Public).

In this last post, I ponder the open question of “What will software testing look like in 2021?” (note: updated the year from 2020 in my original dataset from Answer The Public to 2021).

The reality for most people involved in the software testing business is that testing will look pretty much the same in 2021 as it did in 2020 – and probably as it did for many of the years before that too. Incremental improvements take time in organisations and the scope & impact of such changes will vary wildly between different organisations and even within different parts of the same organisation.

I fully expect 2021 to yield a number of reports about trends in software testing and quality, akin to Capgemini’s annual World Quality Report (which I critiqued again last year). There will probably be a lot of noise around the application of AI and machine learning to testing, especially from tool vendors and the big consultancies.

I feel certain that automation (especially of the “codeless” variety) will continue to be one of the main threads around testing with companies continuing to recruit on the basis of “automated testing” prowess over exploratory testing skills.

I think a small but dedicated community of people genuinely interested in advancing the craft of software testing will continue to publish their ideas and look to inject some reality into the various places that testing gets discussed online.

My daily meditation practice has applications here too. In the same way that the practice helps me to recognise when thoughts are happening without getting caught up in their storyline, I think you should make an effort to observe the inevitable commentary on trends in the testing industry through 2021 without going out of your way to follow them. These trends are likely to change again next year and expending effort trying to keep “on trend” is likely effort better spent elsewhere. Instead, I would recommend focusing on the fundamentals of good software testing, while continuing to demonstrate the value of good testing and advancing the practice as best you can in the context of your organisation.

I would also encourage you to make 2021 the year that you tell your testing stories for the benefit of the wider community – your stories are unique, valuable and a great way for others to learn what’s really going on in our industry. There are many avenues to share your first-person experiences – blog about them, share them as LinkedIn articles, talk about them at meetups or present them at a conference (many of which seem destined to remain as virtual events through 2021, which I see as a positive in terms of widening the opportunity for more diverse stories to be heard).

For some alternative opinions on what 2021 might look like, check out the responses to the recent question “What trends do you think will emerge for testing in 2021?” posed by Ministry of Testing on LinkedIn.

You can find the previous nine parts of this blog series at:

I’ve provided the content in this blog series as part of the “not just for profit” approach of my consultancy business, Dr Lee Consulting. If the way I’m writing about testing resonates with you and you’re looking for help with the testing & quality practices in your organisation, please get in touch and we can discuss whether I’m the right fit for you.

I’m grateful to Paul Seaman and Ky who acted as reviewers for every part of this blog series; I couldn’t have completed the series without their help, guidance and encouragement along the way, thank you!

Thanks also to all those who’ve amplified the posts in this series via their blogs, lists and social media posts – it’s been much appreciated. And, last but not least, thanks to Terry Rice for the underlying idea for the content of this series.

Common search engine questions about testing #9: “Which software testing certification is the best?”

This is the penultimate part of a ten-part blog series in which I will answer some of the most common questions asked about software testing, according to search engine autocomplete results (thanks to Answer The Public).

In this post, I answer the question “Which software testing certification is the best?“.

There has been much controversy around certification in our industry for a very long time. The certification market is dominated by the International Software Testing Qualifications Board (ISTQB), which they describe as “the world’s most successful scheme for certifying software testers”. The scheme arose out of the British Computer Society’s ISEB testing certification in the late 1990s and has grown to become the de facto testing certification scheme. With a million-or-so exams administered and 700,000+ certifications issued, the scheme has certainly been successful in dishing out certifications across its ever-increasing range of offerings (broadly grouped into Agile, Core and Specialist areas).

In the interests of disclosure, I am Foundation certified by the ANZTB and I encouraged all of the testers at Quest in the early-mid 2000s to get certified too. At the time, it felt to me like this was the only certification that gave a stamp of professionalism to testers. After I received education from Michael Bolton during Rapid Software Testing in 2007, I soon realised the errors in my thinking – and then put many of the same testers through RST with James Bach a few years later!

Although the ISTQB scheme has issued many certifications, the value of these certifications is less clear. The lower level certifications, particularly Foundation, are very easy to obtain and require little to no practical knowledge or experience in software testing. It’s been disappointing to witness how this de facto simple certification became a pre-requisite for hiring testers all over the world. The requirement to be ISQTB-certified doesn’t seem to crop up very often on job ads in the Australian market now, though, so maybe its perceived value is falling over time.

If your desire is to become an excellent tester, then I would encourage you to adopt some of the approaches to learning outlined in the previous post in this series. Following a path of serious self-learning about the craft (and maybe challenging yourself with one of the more credible training courses such as BBST or RST) is likely to provide you with much more value in the long-term than ticking the ISTQB certification box. If you’re concerned about your resume “making the cut” when applying for jobs without having ISTQB certification, consider taking Michael Bolton’s advice in No Certification, No Problem!

Coming back to the original question. Imagine what the best software testing certification might be if you happen to be a for-profit training provider for ISTQB certifications. Then think about what the best software testing certification might be if you’re a tester with a few years of experience in the industry looking to take your skills to the next level. I don’t think it makes sense to ask which (of anything) is the “best” as there are so many context-specific factors to consider.

The de facto standard for certification in our industry, viz. ISTQB, is not a requirement for you to become an excellent and credible software tester, in my opinion.

If you’re interested in a much fuller treatment of the issues with testing certifications, I think James Bach has covered all the major arguments in his blog post, Against Certification. Ilari Henrik Aegerter’s short Super Single Slide Sessions #6 – On Certifications video is also worth a look and, for some light relief around this controversial topic, see the IQSTD website!

You can find the first eight parts of this blog series at:

I’m providing the content in this blog series as part of the “not just for profit” approach of my consultancy business, Dr Lee Consulting. If the way I’m writing about testing resonates with you and you’re looking for help with the testing & quality practices in your organisation, please get in touch and we can discuss whether I’m the right fit for you.

Thanks again to my review team (Paul Seaman and Ky) for their helpful feedback on this post, their considerable effort and input as this series comes towards an end has been instrumental in producing posts that I’m proud of.

Common search engine questions about testing #8: “Can I learn software testing on my own?”

This is the eighth of a ten-part blog series in which I will answer some of the most common questions asked about software testing, according to search engine autocomplete results (thanks to Answer The Public).

In this post, I answer the question “Can I learn software testing on my own?” (and the related questions, “Can I learn software testing online?” and “Can anybody learn software testing?”).

The skills needed to be an excellent tester can be learned. How you choose to undertake that learning is a personal choice, but there’s really no need to tackle this substantial task as a solo effort – and I would strongly encourage you not to go it alone. The testing community is strong and, in my experience, exceptionally willing to help people on their journey to becoming better testers so utilizing this vast resource should be part of your strategy. There is so much great content online for free and engaging with great testers is straightforward via, most notably in my opinion, Twitter and LinkedIn.

While it’s great to learn the various techniques and approaches to testing, it’s also worth looking more broadly into fields such as psychology and sociology. Becoming an excellent tester requires more than just great testing and technical skills so broadening your learning should be helpful. While I don’t recommend most of the testing books from “experts”, I’ve made a few recommendations in the Resources section of my consultancy website (and you can find a bunch of blogs, articles, etc. as starting points for further reading there too).

The next part of this blog series will cover the topic of certifications, so I won’t discuss this in depth here – but I don’t believe it’s necessary to undertake the most common certifications in our industry, viz. those offered by the ISTQB. The only formal courses around testing that I choose to recommend are Rapid Software Testing (which I’ve personally attended twice, with Michael Bolton and then James Bach) and the great value Black Box Software Testing courses from the Association for Software Testing.

You can certainly learn the skills required to be an excellent tester and there’s simply no need to go it alone in doing so. There is no need to attend expensive training courses or go through certification schemes on your way to becoming excellent, but you will need persistence, a growth mindset and a keen interest in continuous learning. I recommend leveraging the large, strong and helpful testing community in your journey of learning the craft – engaging with this community has helped me tremendously over many years and I try to give back to it in whatever ways I can, hopefully inspiring and helping more people to experience the awesomeness of the craft of software testing.

You might find the following blog posts useful too in terms of guiding your learning process:

You can find the first seven parts of this blog series at:

I’m providing the content in this blog series as part of the “not just for profit” approach of my consultancy business, Dr Lee Consulting. If the way I’m writing about testing resonates with you and you’re looking for help with the testing & quality practices in your organisation, please get in touch and we can discuss whether I’m the right fit for you.

Thanks again to my awesome review team (Paul Seaman and Ky) for their helpful feedback on this post.

Common search engine questions about testing #7: “Is software testing a good career?”

This is the seventh of a ten-part blog series in which I will answer some of the most common questions asked about software testing, according to search engine autocomplete results (thanks to Answer The Public).

In this post, I answer the question “Is software testing a good career?” (and the related questions, “How is software testing a career?” and “Why choose software testing as a career?”).

Reflecting first on my own experience, software testing ended up being an awesome career. I didn’t set out to become a career software tester, though. After a few years as a developer in the UK, I moved to Australia and started looking for work in the IT industry. Within a couple of weeks of arriving in the country, I landed an interview at Quest Software (then in the Eastern suburbs of Melbourne) for a technical writer position. After interviewing for that position, they mentioned that the “QA Manager” was also looking for people and asked whether I’d be interested in chatting with her also. Long story short, I didn’t land the technical writing job but was offered a “Senior Tester” position – and I accepted it without hesitation! I was simply happy to have secured my first job in a new country, never intending it to be a long-term proposition with Quest or the start of a new career in the field of software testing. As it turned out, I stayed with Quest for 21 years in testing/quality related roles from start to finish!

So, there was some luck involved in finding a good company to work for and a job that I found interesting. I’m not sure I’d have stayed in testing, though, had it not been for the revelation that was attending Rapid Software Testing with Michael Bolton in 2007 – that really gave me the motivation to treat software testing more seriously as a long-term career prospect and also marked the time, in my opinion, that I really started to add much more value to Quest as well. The company appreciated the value that good testers were adding to their development teams and I was fortunate to mentor, train, coach and work alongside some great testers, not only in Australia but all over the world. Looking back on my Quest journey, I think it was the clear demonstration of value from testing that led to more and more opportunities for me (and other testers), as predicted by Steve Martin when he said “be so good they can’t ignore you”!

The landscape has changed considerably in the testing industry over the last twenty years, of course. It has to be acknowledged that it’s becoming very difficult to secure testing roles in which you can expect to perform exploratory testing as the mainstay of your working day (and especially so in higher cost locations). I’ve rarely seen an advertisement for such a role in Australia in the last few years, with most employers now also demanding some “automated testing” skills as part of the job. Whether the reality post-employment is that nearly all testers are now performing a mix of testing (be it scripted, exploratory or a combination of both) and automation development, I’m not so sure. If your desire is to become an excellent (exploratory) tester without having some coding knowledge/experience, then I think there are still some limited opportunities out there but seeking them out will most likely require you to be in the network of people in similar positions in companies that understand the value that testing of this kind can bring.

Making the effort to learn some coding skills is likely to be beneficial in terms of getting your resume over the line. I’d recommend not worrying too much about which language(s)/framework(s) you choose to learn, but rather focusing on the fundamentals of good programming. I would also suggest building an understanding of the “why” and “what” in terms of automation (over the “how”, i.e. which language and framework to leverage in a particular context) as this understanding will allow you to quickly add value and not be so vulnerable to the inevitable changes in language and framework preferences over time.

I think customers of the software we build expect that the software has undergone some critical evaluation by humans before they acquire it, so it both intrigues and concerns me that so many big tech companies publicly express their lack of “testers” as some kind of badge of honour. I simply don’t understand why this is seen as a good thing and it seems to me that this trend is likely to come full (or full-ish) circle at some point when the downsides of removing specialists in testing from the development, release and deployment process outweigh the perceived benefits (not that I’m sure what these are, apart from reduced headcount and cost?).

I still believe that software testing is a good career choice. It can be intellectually challenging, varied and stimulating in the right organization. It’s certainly not getting any easier to secure roles in which you’ll spend all of your time performing exploratory testing, though, so broadening your arsenal to include some coding skills and building a good understanding of why and what makes sense to automate are likely to help you along the way to gaining meaningful employment in this industry.

You can find the first six parts of this blog series at:

I’m providing the content in this blog series as part of the “not just for profit” approach of my consultancy business, Dr Lee Consulting. If the way I’m writing about testing resonates with you and you’re looking for help with the testing & quality practices in your organisation, please get in touch and we can discuss whether I’m the right fit for you.

Thanks again to my erstwhile review team (Paul Seaman and Ky) for their helpful feedback on this post.

Common search engine questions about testing #6: “Is software testing easy?”

This is the sixth of a ten-part blog series in which I will answer some of the most common questions asked about software testing, according to search engine autocomplete results (thanks to Answer The Public).

In this post, I answer the question “Is software testing easy?” (and the related question, “Why is software testing so hard?”).

There exists a perception that “anyone can test” and, since testing is really just “playing with the software”, it’s therefore easy. By contrast, it seems that programming is universally viewed as being difficult. This reasoning leads people to believe that a good place to start their career in IT is as a tester, with a view to moving “up” to the more hallowed ranks of developers.

My experience suggests that many people often have no issue with trying to tell testers how to do their job, in a way that those same people wouldn’t dream of doing towards developers. This generally seems to be based on some past experience from their career when they considered themselves a tester, even if that experience is now significantly outdated and they didn’t engage in any serious work to advance themselves as testers. Such interactions are a red flag that many in the IT industry view testing as the easy part of the software development process.

The perception that testing is easy is also not helped by the existence and prevalence of the simple and easy to achieve ISTQB Foundation certification. The certification has been achieved by several hundred thousand people worldwide (the ISTQB had issued 721000+ certifications as of May 2020, with the vast majority of those likely to be at Foundation level), so it’s clearly not difficult to obtain (even without study) and has flooded the market with “testers” who have little but this certification behind them.

Thanks to Michael Bolton (via this very recent tweet) for identifying another reason why this perception exists. “Testing” is often conflated with “finding bugs” and we all know how easy it is to find bugs in the software we use every day:

There’s a reason that many people think testing is easy, due to an asymmetry. No one ever fired up a computer and stumbled into creating a slick UI or a sophisticated algorithm, but people stumble into bugs every day. Finding bugs is easy, they think. So testing must be easy.

Another unfortunate side effect of the idea that testing is easy is that testers are viewed as fungible, i.e. any tester can simply be replaced by another one since there’s not much skill required to perform the role. The move to outsource testing capability to lower cost locations then becomes an attractive proposition. I’m not going to discuss test outsourcing and offshoring in any depth here, but I’ve seen a lot of great, high value testers around the world lose their jobs due to this process of offshoring based on the misplaced notion of fungibility of testing resources.

Enough about the obvious downsides of mistakenly viewing testing as easy! I don’t believe good software testing is at all easy and hopefully my reasons for saying this will help you to counter any claims that testing (at least, testing as I talk about it) is easy work and can be performed equally well by anyone.

As a good tester, we are tasked with evaluating a product by learning about it through exploration, experimentation, observation and inference. This requires us to adopt a curious, imaginative and critical thinking mindset, while we constantly make decisions about what’s interesting to investigate further and evaluate the opportunity cost of doing so. We look for inconsistencies by referring to descriptions of the product, claims about it and within the product itself. These are not easy things to do.

We study the product and build models of it to help us make conjectures and design useful experiments. We perform risk analysis, taking into account many different factors to generate a wealth of test ideas. This modelling and risk analysis work is far from easy.

We ask questions and provide information to help our stakeholders understand the product we’ve built so that they can decide if it’s the product they wanted. We identify important problems and inform our stakeholders about them – and this is information they sometimes don’t want to hear. Revealing problems (or what might be problems) in an environment generally focused on proving we built the right thing is not easy and requires emotional intelligence & great communication skills.

We choose, configure and use tools to help us with our work and to question the product in ways we’re incapable of (or inept at) as humans without the assistance of tools. We might also write some code (e.g. code developed specifically for the purpose of exercising other code or implementing algorithmic decision rules against specific observations of the product, “checks”), as well as working closely with developers to help them improve their own test code. Using tooling and test code appropriately is not easy.

(You might want to check out Michael Bolton’s Testing Rap, from which some of the above was inspired, as a fun way to remind people about all the awesome things human testers actually do!)

This heady mix of aspects of art, science, sociology, psychology and more – requiring skills in technology, communication, experiment design, modelling, risk analysis, tooling and more – makes it clear to me why good software testing is hard to do.

In wrapping up, I don’t believe that good software testing is easy. Good testing is challenging to do well, in part due to the broad reach of subject areas it touches on and also the range of different skills required – but this is actually good news. The challenging nature of testing enables a varied and intellectually stimulating job and the skills to do it well can be learned.

It’s not easy, but most worthwhile things in life aren’t!

You can find the first five parts of this blog series at:

I’m providing the content in this blog series as part of the “not just for profit” approach of my consultancy business, Dr Lee Consulting. If the way I’m writing about testing resonates with you and you’re looking for help with the testing & quality practices in your organisation, please get in touch and we can discuss whether I’m the right fit for you.

Thanks again to my dedicated review team (Paul Seaman and Ky) for their helpful feedback on this post. Paul’s blog, Not Everybody Can Test, is worth a read in relation to the subject matter of this post.

Common search engine questions about testing #5: “Can you automate software testing?”

This is the fifth of a ten-part blog series in which I will answer some of the most common questions asked about software testing, according to search engine autocomplete results (thanks to Answer The Public).

As I reach the halfway point in this series, I come to the question “Can you automate software testing?” (and the related question, “How can software test automation be done?”).

If you spend any time on Twitter and LinkedIn following threads around testing, this question of whether testing can be automated crops up with monotonous regularity and often seems to result in very heated discussion, with strong opinions from both the “yes” and “no” camps.

As a reminder (from part one of this blog series), my preferred definition of testing comes from Michael Bolton and James Bach, viz.

Testing is the process of evaluating a product by learning about it through experiencing, exploring, and experimenting, which includes to some degree: questioning, study, modelling, observation, inference, etc.

Looking at this definition, testing is clearly a deeply human activity since skills such as learning, exploring, questioning and inferring are not generally those well modelled by machines (even with AI/ML). Humans may or may not be assisted by tools or automated means while exercising these skills, but that doesn’t mean that the performance of testing is itself “automated”.

The distinction drawn between “testing” and “checking” made by James Bach and Michael Bolton has been incredibly helpful for me when talking about automation and countering the idea that testing can be automated (much more so than “validation” and “verification” in my experience). As a refresher, their definition of checking is:

Checking is the process of making evaluations by applying algorithmic decision rules to specific observations of a product.

As Michael says, “We might choose to automate the exercise of some functions in a program, and then automatically compare the output of that program with some value obtained by another program or process. I’d call that a check.” Checking is a valuable component of our overall testing effort and, by this definition, lends itself to be automated. But the binary evaluations resulting from the execution of such checks form only a small part of the testing story and there are many aspects of product quality that are not amenable to such black and white evaluation.

Thinking about checks, there’s a lot that goes into them apart from the actual execution (by a machine or otherwise): someone decided we needed a check (risk analysis), someone designed the check, someone implemented the check (coding), someone decided what to observe and how to observe it, and someone evaluated the results from executing the check. These aspects of the check are testing activities and, importantly, they’re not the aspects that can be given over to a machine, i.e. be automated. There is significant testing skill required in the design, implementation and analysis of the check and its results, the execution (the automated bit) is really the easy part.

To quote Michael again:

A machine producing a bit is not doing the testing; the machine, by performing checks, is accelerating and extending our capacity to perform some action that happens as part of the testing that we humans do. The machinery is invaluable, but it’s important not to be dazzled by it. Instead, pay attention to the purpose that it helps us to fulfill, and to developing the skills required to use tools wisely and effectively.

We also need to be mindful to not conflate automation in testing with “automated checking”. There are many other ways that automation can help us, extending human abilities and enabling testing that humans cannot practically perform. Some examples of applications of automation include test data generation, test environment creation & configuration, software installation & configuration, monitoring & logging, simulating large user loads, repeating actions en masse, etc.

If we make the mistake of allowing ourselves to believe that “automated testing” exists, then we can all too easily fall into the trap of narrowing our thinking about testing to just automated checking, with a resulting focus on the development and execution of more and more automated checks. I’ve seen this problem many times across different teams in different geographies, especially so in terms of regression testing.

I think we are well served to eliminate “automated testing” from our vocabulary, instead talking about “automation in testing” and the valuable role automation can play in both testing & checking. The continued propaganda around “automated testing” as a thing, though, makes this job much harder than it sounds. You don’t have to look too hard to find examples of test tool vendors using this term and making all sorts of bold claims about their “automated testing solutions”. It’s no wonder that so many testers remain confused in answering the question about whether testing can be automated when a quick Google search got me to some of these gems within the top few results: What is automated testing? (SmartBear), Automated software testing (Atlassian) and Test Automation vs. Automated Testing: The Difference Matters (Tricentis).

I’ve only really scratched the surface of this big topic in this blog, but it should be obvious by now that I don’t believe you can automate software testing. There is often value to be gained by automating checks and leveraging automation to assist and extend humans in their testing efforts, but the real testing lies with the humans – and always will.

Some recommended reading related to this question:

  • The Testing and Checking Refined article by James Bach and Michael Bolton, in which the distinction between testing and checking is discussed in depth, as well as the difference between checks performed by humans and those by machines.
  • The Automation in Testing (AiT) site by Richard Bradshaw and Mark Winteringham, their six principles of AiT make a lot of sense to me.
  • Bas Dijkstra’s blog

You can find the first four parts of this blog series at:

I’m providing the content in this blog series as part of the “not just for profit” approach of my consultancy business, Dr Lee Consulting. If the way I’m writing about testing resonates with you and you’re looking for help with the testing & quality practices in your organisation, please get in touch and we can discuss whether I’m the right fit for you.

Thanks again to my awesome review team (Paul Seaman and Ky) for their helpful feedback on this post.

Common search engine questions about testing #4: “How is software testing done?”

This is the fourth of a ten-part blog series in which I will answer some of the most common questions asked about software testing, according to search engine autocomplete results (thanks to Answer The Public).

In this post, I ponder the question “How is software testing done?” (and the related questions, “What are software testing methodologies?”, “What is the software testing life cycle?” and “What is the software testing process?”).

There are many different ways in which software testing is performed, by different people in different organizations with different ideas about what constitutes “good testing”. Don’t be fooled into believing there is “one way” to do testing! There is certainly no single, approved, credible and official way to perform testing – and this is actually a good thing, in my opinion.

So, the question should perhaps be “How might software testing be done?” and, in answering this question, the idea of context is paramount. James Bach defines “context” (in Context-Driven Methodology) as follows:

When I say “context” I mean the totality of a situation that influences the success or failure of an enterprise.

(and Dictionary.com similarly offers “the set of circumstances or facts that surround a particular event, situation, etc.”) The first principle of Context-Driven Testing says “The value of any practice depends on its context.” The way you would approach the testing of a medical device (where a defect could result in loss of life) is likely quite different to how you would test a website for a local business, for example. The context is different – and the differences are important.

While there may be books or certifications that propose a “testing process” or methodology, you should consider the context of your particular situation to assess whether any of these processes or methodologies have valuable elements to leverage. Remember that testing requires a broad variety of different skills and activities: working with other people, formulating hypotheses, creating & changing strategies, critical thinking & evaluation, finding the right people when you need help, assessing what all this might mean for risk and then finding ways to relate this information in compelling and credible ways. What we need is a way of thinking about testing that is flexible enough to cover such a range of skills and activities across many different contexts.

The following from context-driven-testing.com puts it well, I think:

Context-driven testers choose their testing objectives, techniques, and deliverables (including test documentation) by looking first to the details of the specific situation, including the desires of the stakeholders who commissioned the testing. The essence of context-driven testing is project-appropriate application of skill and judgment. The Context-Driven School of testing places this approach to testing within a humanistic social and ethical framework.

Ultimately, context-driven testing is about doing the best we can with what we get. Rather than trying to apply “best practices,” we accept that very different practices (even different definitions of common testing terms) will work best under different circumstances.

Bearing the above in mind, the only software testing methodology that I feel comfortable to recommend is Rapid Software Testing (RST) developed by James Bach and Michael Bolton. RST isn’t a prescriptive process but rather a way to understand testing with a focus on context and people:

[RST] is a responsible approach to software testing, centered around people who do testing and people who need it done. It is a methodology (in the sense of “a system of methods”) that embraces tools (aka “automation”) but emphasizes the role of skilled technical personnel who guide and drive the process.

Rather than being a set of templates and rules, RST is a mindset and a skill set. It is a way to understand testing; it is a set of things a tester knows how to do; and it includes approaches to effective leadership in testing.

https://rapid-software-testing.com/about-rapid-software-testing/

RST is therefore quite different from some of the prevalent processes/methodologies that you might come across in searching for resources to answer the question of how testing is done, such as ISTQB and TMap. These systems are often referred to as “factory-style testing” and an excellent summary of how RST differs from these can be found at https://www.satisfice.com/download/how-rst-is-different-from-factory-style-testing

Given how different your context and testing mission is likely to be on different projects in different organizations at different times for different customers, the way “testing is done” necessarily needs to be flexible and adaptable enough to respect these very different situations. Any formal process or methodology that seeks to prescribe how to test is likely to be sub-optimal in your particular context, so I suggest adopting something like the mindset proposed by RST and adapting your approach to testing to suit your context.

You can find the first three parts of this blog series at:

I’m providing the content in this blog series as part of the “not just for profit” approach of my consultancy business, Dr Lee Consulting. If the way I’m writing about testing resonates with you and you’re looking for help with the testing & quality practices in your organisation, please get in touch and we can discuss whether I’m the right fit for you.

Thanks again to my patient and dependable review team (Paul Seaman and Ky) for their helpful feedback on this post.

“Calling Bullsh*t” (Carl T. Bergstrom and Jevin D. West)

It was thanks to a recommendation from Michael Bolton that I came across the book Calling Bullsh*t by Carl T. Bergstrom and Jevin D. West. While it’s not a book specifically about software testing, there are some excellent takeaways for testers as I’ll point to in the following review of the book. This book is a must read for software testers in my opinion.

The authors’ definition of bullshit (BS) is important to note before digging into the content (appearing on page 40):

Bullshit involves language, statistical figures, data graphics, and other forms of presentation intended to persuade or impress an audience by distracting, overwhelming, or intimidating them with a blatant disregard for truth, logical coherence, or what information is actually being conveyed.

I was amazed to read that the authors already run a course at a US university on the same topic as this book:

We have devoted our careers to teaching students how to think logically and quantitatively about data. This book emerged from a course we teach at the University of Washington, also titled “Calling Bullshit”. We hope it will show you that you do not need to be a professional statistician or econometrician or data scientist to think critically about quantitative arguments, nor do you need extensive data sets and weeks of effort to see through bullshit. It is often sufficient to apply basic logical reasoning to a problem and, where needed, augment that with information readily discovered via search engine.

The rise of the internet and particularly social media are noted as ways that BS has proliferated in more recent times, spreading both misinformation (claims that are false but not deliberately designed to deceive) and disinformation (deliberate falsehoods).

…the algorithms driving social media content are bullshitters. They don’t care about the messages they carry. They just want our attention and will tell us whatever works to capture it.

Bullshit spreads more easily in a massively networked, click-driven social media world than in any previous social environment. We have to be alert for bullshit in everything we read.

As testers, we tend to have a critical thinking mindset and are hopefully alert to stuff that just doesn’t seem right, whether that’s the way a feature works in a product or a claim made about some software. It seems to me that testers should naturally be good spotters of BS more generally and this book provides a lot of great tips both for spotting BS and learning how to credibly refute it.

Looking at black boxes (e.g. statistical procedures or data science algorithms), the authors make the crucial point that understanding the inner workings of the black box is not required in order to spot problems:

The central theme of this book is that you usually don’t have to open the analytic black box in order to call bullshit on the claims that come out of it. Any black box used to generate bullshit has to take in data and spit results out.

Most often, bullshit arises either because there are biases in the data that get fed into the black box, or because there are obvious problems with the results that come out. Occasionally the technical details of the black box matter, but in our experience such cases are uncommon. This is fortunate, because you don’t need a lot of technical expertise to spot problems with the data or results. You just need to think clearly and practice spotting the sort of thing that can go wrong.

The first big topic of consideration looks at associations, correlations and causes and spotting claims that confuse one for the other. The authors provide excellent examples in this chapter of the book and a common instance of this confusion in the testing arena is covered by Theresa Neate‘s blog post, Testing and Quality: Correlation does not equal Causation. (I’ve also noted the confusion between correlation and causality very frequently when looking at big ag-funded “studies” used as ammunition against veganism.)

The chapter titled “Numbers and Nonsense” covers the various ways in which numbers are used in misleading and confusing ways. The authors make the valid point that:

…although numbers may seem to be pure facts that exist independently from any human judgment, they are heavily laden with context and shaped by decisions – from how they are calculated to the units in which they are expressed.

It is all too common in the testing industry for people to hang numbers on things that make little or no sense to look at quantitatively, counting “test cases” comes to mind. The book covers various ways in which numbers turn into nonsense, including summary statistics, percentages and percentage points. Goodhart’s Law is mentioned (in its rephrased form by Marilyn Strathern):

When a measure becomes a target, it ceases to be a good measure

I’m sure many of us are familiar with this law in action when we’re forced into “metrics programmes” around testing for which gaming becomes the focus rather than the improvement our organizations were looking for. The authors introduce the idea of mathiness here: “mathiness refers to formulas and expressions that may look and feel like math – even as they disregard the logical coherence and formal rigour of actual mathematics” and testing is not immune from mathiness either, e.g. “Tested = Checked + Explored” is commonly quoted from Elisabeth Hendrickson‘s (excellent) Explore It! book. Another concept that will be very familiar to testers (and others in the IT industry) is zombie statistics, viz.

…numbers that are cited badly out of context, are sorely outdated, or were entirely made up in the first place – but they are quoted so often that they simply won’t die.

There are many examples of such zombie statistics in our industry, Boehm’s so-called cost of change curve being a prime example (claiming that the cost of changes later in the development cycle is orders of magnitude higher than earlier in the cycle) and is of one the examples covered beautifully in Laurent Bossavit’s excellent book, The Leprechauns of Software Engineering.

The next statistical concept introduced in the book is selection bias and I was less familiar with this concept (at least under this name):

Selection bias arises when the individuals that you sample for your study differ systematically from the population of individuals eligible for your study.

This sort of non-random sampling leads to statistical analyses failing or becoming misleading and there are again some well-considered examples to explain and illustrate this bias. Reading this chapter brought to mind my recent critique of the Capgemini World Quality Report in which I noted that both the size of organizations and roles of participants in the survey was problematic. (I again note that from my vegan research that many big ag-funded studies suffer from this bias too.)

A hefty chapter is devoted to data visualization, with the authors noting the relatively recent proliferation of charts and data graphics in the media due to the technology becoming available to more easily produce them. The treatment of the various ways that charts can be misleading is again excellent with sound examples (including axis scaling, axis starting values, and the “binning” of axis values). I loved the idea of glass slippers here, viz.

Glass slippers take one type of data and shoehorn it into a visual form designed to display another. In doing so, they trade on the authority of good visualizations to appear authoritative themselves. They are to data visualizations what mathiness is to mathematical equations.

The misuse of the periodic table visualization is cited as an example and, of course, the testing industry has its own glass slippers in this area, for example Santhosh Tuppad’s Heuristic Table of Testing! This chapter also discusses visualizations that look like Venn diagrams but aren’t, and highlights the dangers of 3-D bar graphs, line graphs and pie charts. A new concept for me in this chapter was the principle of proportional ink:

Edward Tufte…in his classic book The Visual Display of Quantitative Information…states that “the representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the numerical quantities represented.” The principle of proportional ink applies this rule to how shading is used on graphs.

The illustration of this principle by well-chosen examples is again very effective here.

It’s great to see some sensible commentary on the subject of big data in the next chapter. The authors say “We want to provide an antidote to [the] hype” and they certainly achieve this aim. They discuss AI & ML and the critical topic of how training data influences outcomes. They also note how machine learning algorithms perpetuate human biases.

The problem is the hype, the notion that something magical will emerge if only we can accumulate data on a large enough scale. We just need to be reminded: Big data is not better; it’s just bigger. And it certainly doesn’t speak for itself.

The topics of Big Data, AI and ML are certainly hot in the testing industry at the moment, with tool vendors and big consultancies all extoling the virtues of these technologies to change the world of testing. These claims have been made for quite some time now and, as I noted in my critique of the Capgemini World Quality Report recently, the reality has yet to catch up with the hype. I commend the authors here for their reality check in this over-hyped area.

In the chapter titled “The Susceptibility of Science”, the authors discuss the scientific method and how statistical significance (p-values) is often manipulated to aid with getting research papers published in journals. Their explanation of the base rate fallacy is excellent and a worthy inclusion, as it is such a common mistake. While the publication of dodgy papers and misleading statistics are acknowledged, the authors’ belief is that “science just plain works” – and I agree with them. (From my experience in vegan research, I’ve read so many dubious studies funded by big ag but these don’t undermine my faith in science, rather my faith in human nature sometimes!) In closing:

Empirically, science is successful. Individual papers may be wrong and individual studies misreported in the popular press, but the institution as a whole is strong. We should keep this in perspective when we compare science to much of the other human knowledge – and human bullshit – that is out there.

In the penultimate chapter, “Spotting Bullshit”, the discussion of the various means by which BS arises (covered throughout the book) is split out into six ways of spotting it, viz.

  • Question the source of information
  • Beware of unfair comparisons
  • If it seems too good or bad to be true…
  • Think in orders of magnitude
  • Avoid confirmation bias
  • Consider multiple hypotheses

These ways of spotting BS act as a handy checklist I think and will certainly be helpful to me in refining my skills in this area. While I was still reading this book, I listened to a testing panel session online and one of the panelists was from testing tool vendor, Applitools. He briefly mentioned some claims about their visual AI-powered test automation tool. These claims piqued my interest and I managed to find the same statistics on their website:

Applitools claims about their visual AI-powered test automation tool

I’ll leave it as an exercise for the reader to decide if any of the above falls under the various ways BS manifests itself according to this book!

The final chapter, “Refuting Bullshit”, is really a call to action:

…a solution to the ongoing bullshit epidemic is going to require more than just an ability to see it for what it is. We need to shine a light on bullshit where it occurs, and demand better from those who promulgate it.

The authors provide some methods to refute BS, as they themselves use throughout the book in the many well-chosen examples used to illustrate their points:

  • Use reductio ad absurdum
  • Be memorable
  • Find counterexamples
  • Provide analogies
  • Redraw figures
  • Deploy a null model

They also “conclude with a few thoughts about how to [call BS] in an ethical and constructive manner”, viz.

  • Be correct
  • Be charitable
  • Admit fault
  • Be clear
  • Be pertinent

In summary, this book is highly recommended reading for all testers to help them become more skilled spotters of BS; be that from vendors, testing consultants or others presenting information about testing. This skill will also come in very handy in spotting BS in claims made about the products you work on in your own organization!

The amount of energy needed to refute bullshit is an order of magnitude bigger than [that needed] to produce it.

Alberto Brandolini (Italian software engineer, 2014)

After reading this book, you should have the skills to spot BS and I actively encourage you to then find inventive ways to refute it publicly so that others might not get fooled by the same BS.

Our industry needs those of us who genuinely care about testing to call out BS when we see it, I’m hoping to see more of this in our community! (My critique of the Capgemini World Quality Report and review of a blog post by Cigniti are examples of my own work in this area as I learn and refine these skills.)