Software estimation techniques are critical to the success of any project.
Accurately estimating the time, resources, and cost required to complete a project during software planning can mean the difference between success and failure.
This is especially true for SaaS companies, where timely delivery of high-quality software is essential for customer satisfaction and business growth.
However, not all effort estimation techniques are created equal.
Some are more useful than others, and some are more problematic than others.
I'm going to explain and offer some thoughts on 5 useful software estimation techniques, and 5 that you might want to avoid.
5 Good Estimation Techniques
Every software development team can benefit from understanding a range of techniques.
While it's true that software estimation methods have some inherent flaws and pitfalls due to the limitations of human experience and intuition, a combination of techniques can help mitigate estimation risks. It increases the chance your teams will make informed decisions, manage risks, and deliver projects on time and within budget.
Teams should review the product backlog in whatever project management tools you use, whether that be Jira, Asana, Linear or something else, and have a thoughtful and routine effort estimation process.
You’ll notice that there are pitfalls in every estimation technique. My view is that AI will soon be ready to help estimate far better. Understanding different ways of estimating will help us to be critical in how we think about AI-assisted estimation. More on that later.
- Planning Poker
Planning Poker is a consensus-based estimation technique.
During a Planning Poker session, team members estimate the effort required for each task using a deck of cards with numerical values. The team then discusses their estimates and tries to come to a consensus for a story point estimate. This process continues until all the tasks are estimated.
- Bucket System
This one's really similar to our T-shirts. It works by dividing the project into buckets or categories based on the level of effort required. Too much lack of clarity, too much opinion.
- Affinity Mapping
Affinity Mapping is used to estimate by grouping tasks based on their similarities. Grouping is, again, too open to interpretation and usually based on intuitions and not data. Complexity and uncertainty simply fly under the radar.
- Dot Voting
Dot Voting estimates the effort required to complete a project by allowing team members to vote on their estimation using dots. Same stuff – subjective opinions with no built-in chance to surface underlying complexity or uncertainty.
The future of estimation
Human estimation is flawed. We can't help it. Our experiences and cognitive limitations bias us. This results in inaccurate and unreliable predictions.
I think AI-powered prediction models are a solution ready to change widespread change.
They leverage vast amounts of data and sophisticated algorithms to generate highly accurate predictions that are free from human bias. In sports, for example, teams that use AI-powered prediction models have a significant competitive advantage over those that rely on human estimation alone.
These models analyse large amounts of historical data, identify patterns and trends that humans might miss, and generate reliable predictions for everything from player performance to game outcomes.
As AI-powered prediction models continue to evolve and improve, they will become an essential project planning tool for any organisation that wants to stay ahead of the competition. Optimised resource management and task management will rely on AI.
Those that fail to adopt these technologies risk falling behind and losing their competitive edge. In short, the future belongs to those who embrace AI-powered prediction models.
Conclusion
We all know the impact bad estimates has on projects. No one estimation technique is the answer. Combining them and understanding how they work increases our chances of success.
I'm excited about the use of Artificial Intelligence (AI) for software estimation. AI with access to reams of rich data could, in principle, offer radically better estimates that stop projects getting out of hand.
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