Skip to main content

Computational Stress in Production


Last night I attended MiniCAST, an online version of the Association for Software Testing's famous CAST conference. I've never been to CAST in person but I can say that the vibe here was great, much more informal and peer-based than the presenter-audience split I've seen elsewhere. It ran for four hours and squeezed in four talks on two tracks, several socialising sessions, and a keynote from Rachel Kibler.

Rachel spoke about stress cases, those scenarios when context, or the product, or both in tandem distress the user. For example, the health-tracking app that excluded women because it didn't include menstrual cycles, or the social media app that pushed a daughter's photo into a dad's timeline with a celebratory whoop ... on the anniversary of her death, or the ride-share app with numerous pop-ups that is hard to use in the dark, walking fast, with low battery, trying to get a lift out of a bad neighbourhood.

These kinds of threats to inclusivity, emotional stability, and personal security are seen in development process with low diversity, a focus on success, and a lack of interest in users and their real life situations. 

While not always common, stress cases should not be dismissed as simple edge cases (traditionally, a situation where some parameter is pushed to an extreme value). They affect real people in real, tangible, consequential ways. In our ROI-driven world this may not be enough of an argument for some software producers, but the potential for reputational damage probably is.

To help to avoid cases of stress in the wild, Rachel suggested a few approaches in development:

  • Have a designated dissenter, someone whose role is to look for the flaws, find the stress points, advocate for those who find themselves off the happy path.
  • Run pre-mortems, where the potential bad outcomes are written up as headlines and then routes to avoid them are found.
  • Read copy aloud in a bright voice. How does it sound when the content doesn't fit that medium?
  • Give some of your personas traumatic back history.
  • Put yourself under stress when testing. How does that feel? Where does the product fail (you)?
  • Be bold in telling management to be kind, considerate, and ethical.

Remember, there is no average user and someone is always having a bad day.


 Sarah Aslanifar talked about computational thinking which she described as:
an iterative system of generative reasoning in which people build models of a subject in a notation capable of being executed objectively and automatically be a machine, with observable and falsifiable output.
This style of thinking is the result of a logical progression from concrete to abstract thought through human history: oral, written, and now computational. As I understood it, at each stage it was possible for there to be dialogue at a greater remove from reality and at a greater distance between participants.

We're in the computational phase now and our abstractions, or models, have the potential to be encoded and executed. Monte Carlo simulation, where scenarios are run numerous times to understand the space of possible outcomes from some starting situation and with some set of constraints, might be an example.

I don't think Sarah said it explicitly, but the key thing here seems to be the use of the computer as a tool to aid thinking. Exercising a model independently of our own heads gives us a chance to reflect on where it is successful and where it deviates from reality. Analysis of the results can help us to determine what to alter to try to make it better.

Machine learning seems like an interesting area of this space. It is notoriously hard to interrogate, although it is certainly possible to experiment with parameters to improve its outcomes. A generate-and-test strategy is reasonable for exploring an unknown area, but it's not clear to me that it would qualify as computational thinking, not least because of the falsifiability requirement in Sarah's definition.

Perhaps I should have asked Alex Eftimiades about that. He presented on the challenges and value of testing machine learning systems in production. Production for him is financial systems, and the goal of his work is to inspect the firehose of data looking for potentially fraudulent transactions.


One of the points he made early on was that in the "traditional" software testing world, there is a culture of binary pass/fail decisions, where a fail typically indicates some kind of bug. In the machine learning world that sharp distinction is smooshed out into a probability distribution where uncertainty around a result is the norm.

Without a guillotine oracle, the approaches open to testers are to question performance and divergences. These are still comparisons, because testing is about finding differences that make a difference, but they are now statistical in nature. 

Without going into the technical weeds too much, Alex asked questions like does the performance of the system on its training and production data differ by an amount that is not explained by baseline variation? If I tweak the inputs to the system in known ways does the output of the system change in step in ways that are explainable and reasonable? Can I create a threshold for alerting by adjusting it until the balance of true and false positives is acceptable to me, in this context, at this time?

Comments

Popular posts from this blog

Can Code, Can't Code, Is Useful

The Association for Software Testing is crowd-sourcing a book,  Navigating the World as a Context-Driven Tester , which aims to provide  responses to common questions and statements about testing from a  context-driven perspective . It's being edited by  Lee Hawkins  who is  posing questions on  Twitter ,   LinkedIn , Mastodon , Slack , and the AST  mailing list  and then collating the replies, focusing on practice over theory. I've decided to  contribute  by answering briefly, and without a lot of editing or crafting, by imagining that I'm speaking to someone in software development who's acting in good faith, cares about their work and mine, but doesn't have much visibility of what testing can be. Perhaps you'd like to join me?   --00-- "If testers can’t code, they’re of no use to us" My first reaction is to wonder what you expect from your testers. I am immediately interested in your working context and the way

Meet Me Halfway?

  The Association for Software Testing is crowd-sourcing a book,  Navigating the World as a Context-Driven Tester , which aims to provide  responses to common questions and statements about testing from a  context-driven perspective . It's being edited by  Lee Hawkins  who is  posing questions on  Twitter ,   LinkedIn , Mastodon , Slack , and the AST  mailing list  and then collating the replies, focusing on practice over theory. I've decided to  contribute  by answering briefly, and without a lot of editing or crafting, by imagining that I'm speaking to someone in software development who's acting in good faith, cares about their work and mine, but doesn't have much visibility of what testing can be. Perhaps you'd like to join me?   --00-- "Stop answering my questions with questions." Sure, I can do that. In return, please stop asking me questions so open to interpretation that any answer would be almost meaningless and certa

Testing (AI) is Testing

Last November I gave a talk, Random Exploration of a Chatbot API , at the BCS Testing, Diversity, AI Conference .  It was a nice surprise afterwards to be offered a book from their catalogue and I chose Artificial Intelligence and Software Testing by Rex Black, James Davenport, Joanna Olszewska, Jeremias Rößler, Adam Leon Smith, and Jonathon Wright.  This week, on a couple of train journeys around East Anglia, I read it and made sketchnotes. As someone not deeply into this field, but who has been experimenting with AI as a testing tool at work, I found the landscape view provided by the book interesting, particularly the lists: of challenges in testing AI, of approaches to testing AI, and of quality aspects to consider when evaluating AI.  Despite the hype around the area right now there's much that any competent tester will be familiar with, and skills that translate directly. Where there's likely to be novelty is in the technology, and the technical domain, and the effect of

Testers are Gate-Crashers

  The Association for Software Testing is crowd-sourcing a book,  Navigating the World as a Context-Driven Tester , which aims to provide  responses to common questions and statements about testing from a  context-driven perspective . It's being edited by  Lee Hawkins  who is  posing questions on  Twitter ,   LinkedIn , Mastodon , Slack , and the AST  mailing list  and then collating the replies, focusing on practice over theory. I've decided to  contribute  by answering briefly, and without a lot of editing or crafting, by imagining that I'm speaking to someone in software development who's acting in good faith, cares about their work and mine, but doesn't have much visibility of what testing can be. Perhaps you'd like to join me?   --00-- "Testers are the gatekeepers of quality" Instinctively I don't like the sound of that, but I wonder what you mean by it. Perhaps one or more of these? Testers set the quality sta

Postman Curlections

My team has been building a new service over the last few months. Until recently all the data it needs has been ingested at startup and our focus has been on the logic that processes the data, architecture, and infrastructure. This week we introduced a couple of new endpoints that enable the creation (through an HTTP POST) and update (PUT) of the fundamental data type (we call it a definition ) that the service operates on. I picked up the task of smoke testing the first implementations. I started out by asking the system under test to show me what it can do by using Postman to submit requests and inspecting the results. It was the kinds of things you'd imagine, including: submit some definitions (of various structure, size, intent, name, identifiers, etc) resubmit the same definitions (identical, sharing keys, with variations, etc) retrieve the submitted definitions (using whatever endpoints exist to show some view of them) compare definitions I submitted fro

Build Quality

  The Association for Software Testing is crowd-sourcing a book,  Navigating the World as a Context-Driven Tester , which aims to provide  responses to common questions and statements about testing from a  context-driven perspective . It's being edited by  Lee Hawkins  who is  posing questions on  Twitter ,   LinkedIn , Mastodon , Slack , and the AST  mailing list  and then collating the replies, focusing on practice over theory. I've decided to  contribute  by answering briefly, and without a lot of editing or crafting, by imagining that I'm speaking to someone in software development who's acting in good faith, cares about their work and mine, but doesn't have much visibility of what testing can be. Perhaps you'd like to join me?   --00-- "When the build is green, the product is of sufficient quality to release" An interesting take, and one I wouldn't agree with in general. That surprises you? Well, ho

Make, Fix, and Test

A few weeks ago, in A Good Tester is All Over the Place , Joep Schuurkes described a model of testing work based on three axes: do testing yourself or support testing by others be embedded in a team or be part of a separate team do your job or improve the system It resonated with me and the other testers I shared it with at work, and it resurfaced in my mind while I was reflecting on some of the tasks I've picked up recently and what they have involved, at least in the way I've chosen to address them. Here's three examples: Documentation Generation We have an internal tool that generates documentation in Confluence by extracting and combining images and text from a handful of sources. Although useful, it ran very slowly or not at all so one of the developers performed major surgery on it. Up to that point, I had never taken much interest in the tool and I could have safely ignored this piece of work too because it would have been tested by

Am I Wrong?

I happened across Exploratory Testing: Why Is It Not Ideal for Agile Projects? by Vitaly Prus this week and I was triggered. But why? I took a few minutes to think that through. Partly, I guess, I feel directly challenged. I work on an agile project (by the definition in the article) and I would say that I use exclusively exploratory testing. Naturally, I like to think I'm doing a good job. Am I wrong? After calming down, and re-reading the article a couple of times, I don't think so. 😸 From the start, even the title makes me tense. The ideal solution is a perfect solution, the best solution. My context-driven instincts are reluctant to accept the premise, and I wonder what the author thinks is an ideal solution for an agile project, or any project. I notice also that I slid so easily from "an approach is not ideal" into "I am not doing a good job" and, in retrospect, that makes me smile. It doesn't do any harm to be reminded that your cognitive bias

Test Now

The Association for Software Testing is crowd-sourcing a book,  Navigating the World as a Context-Driven Tester , which aims to provide  responses to common questions and statements about testing from a  context-driven perspective . It's being edited by  Lee Hawkins  who is  posing questions on  Twitter ,   LinkedIn , Mastodon , Slack , and the AST  mailing list  and then collating the replies, focusing on practice over theory. I've decided to  contribute  by answering briefly, and without a lot of editing or crafting, by imagining that I'm speaking to someone in software development who's acting in good faith, cares about their work and mine, but doesn't have much visibility of what testing can be. Perhaps you'd like to join me?   --00-- "When is the best time to test?" Twenty posts in , I hope you're not expecting an answer without nuance? You are? Well, I'll do my best. For me, the best time to test is when there

Vanilla Flavour Testing

I have been pairing with a new developer colleague recently. In our last session he asked me "is this normal testing?" saying that he'd never seen anything like it anywhere else that he'd worked. We finished the task we were on and then chatted about his question for a few minutes. This is a short summary of what I said. I would describe myself as context-driven . I don't take the same approach to testing every time, except in a meta way. I try to understand the important questions, who they are important to, and what the constraints on the work are. With that knowledge I look for productive, pragmatic, ways to explore whatever we're looking at to uncover valuable information or find a way to move on. I write test notes as I work in a format that I have found to be useful to me, colleagues, and stakeholders. For me, the notes should clearly state the mission and give a tl;dr summary of the findings and I like them to be public while I'm working not just w