TestCon Vilnius 2018
Date: 16 October, 2018
Venue: Crowne Plaza Vilnius – M. K. Čiurlionio str. 84, Vilnius, Lithuania
Due to the limited availability of seats, early registration is strongly recommended to ensure your participation – we rely on a first-come, first-served basis.
Dr Andrew Brown is a principal technical consultant at SQS. Recently, he has developed an independent line of research into understanding why we humans make the mistakes that lead to software defects.
He has 25 years’ experience in the software industry. Previous roles include Head of QA at HMV, Head of QA at a financial software house and a test manager in Japan.
He holds a degree in Physics and Maths, an MBA from Warwick Business School and a doctorate from Imperial College.
Improve Planning Estimates through Cognitive Bias Reduction
Are you puzzled why your estimate turned out wrong, or stressed from working to an impossible deadline? Teams on inaccurately estimated projects suffer stress, burnout, and poor quality, as pressure is applied to recover to an unrealistic schedule. Such project teams also descend into irrational decision-making, with potentially catastrophic consequences. Frustratingly, even if we perform well, we are still judged by our failure to meet an impossible deadline.
In this workshop we learn how estimation errors are caused not just by new technology or intentionally manipulated estimations, but also arise normally from limitations in the way we think. We discover how cognitive biases contribute towards estimation errors, and what we can do to mitigate these biases.
But beware! There are benefits and disadvantages of doing so. Learn how the planning fallacy, anchoring effect, and optimistic bias contribute towards estimation errors and lead to irrational decision-making. Discover the paradox of past experience, where instead of aiding prediction, our experience frequently confounds us. Experience how a planning scenario game and other tools can reduce your estimation errors.
Take away ideas to make your estimates more accurate and less risky by spotting distortions creep into your estimates, then reduce those distortions by addressing the underlying cognitive biases.
Introduction to the problem (0.5 hour)
- Reasons why we estimate
- The typical outcome – underestimation
- Consequences of underestimation – resourcing, planning, decision, and risk-taking failures
- Understanding causes of estimation error – technology uncertainty, intentional manipulation, unconscious biases
Exploring the cognitive biases affecting estimation (1.5 hour)
- Anchoring effect
- Planning fallacy
- Optimistic bias
- Overconfidence effect
- Other biases affecting estimation
Exploring the planning fallacy (1.5 hour)
- Why we focus on plan-based scenarios, rather than relevant past experience
- Obstacles to using our past experience
- The two types of information used for task estimation
The planning game (2 hour)
- Iterative game, focused on the estimation of tasks that can be run within a given time
- Review and feedback
- Game re-run, but with debiasing approaches introduced
- Game de-brief
Improving our estimates (0.5 hour)
- Improving the accuracy and utility of an estimate
- Avoiding the adverse effects of overestimation
- Effective use of deadlines
- How to use an independent assessor – the actor-observer effect
- How to remove barriers to using our past experience
On completion of this course, you will be able to identify estimation situations where your judgement is vulnerable to distortion by cognitive biases. You will be able to identify the nature of that distortion and how it may impact your subsequent estimation. You will learn a number of mitigation tools and techniques to reduce your vulnerability to these biases and reduce their impact.
You will also learn how distorted estimations can potentially impact downstream project behavior, leading to unintentional risk taking.
After this course, you can use your knowledge of how cognitive biases to improve the accuracy and value of you estimates.
This workshop is aimed at people involved in estimation, both those who produce estimates and also those who consume estimates.
Level 2 – Intermediate material. Assumes knowledge and provides specific details about the topic.