The author draws a parallel between estimating software development time and a washing machine's displayed remaining time. Just as a washing machine constantly recalculates its estimated completion time based on real-time factors, software estimation should be a dynamic, ongoing process. Instead of relying on initial, often inaccurate, predictions, we should embrace the inherent uncertainty of software projects and continuously refine our estimations based on actual progress and newly discovered information. This iterative approach, acknowledging the evolving nature of development, leads to more realistic expectations and better project management.
The author of the blog post, "My washing machine refreshed my thinking on software effort estimation," draws a parallel between the seemingly simple task of estimating the remaining time on a washing machine cycle and the often complex process of estimating software development effort. The author observes that, much like with software projects, initial estimates for wash cycles are often overly optimistic and subject to unexpected variations. The washing machine, initially displaying a short completion time, frequently adjusts its estimate upwards throughout the cycle, a behavior mirroring the "planning fallacy" and the tendency for developers to underestimate the actual time required for coding tasks.
The blog post meticulously dissects the reasons behind this estimation discrepancy. It argues that, similar to software development, laundry involves a series of interconnected sub-tasks, each with its own inherent uncertainties. Just as a software project might encounter unforeseen bugs or integration challenges, a laundry cycle might be delayed by unbalanced loads, excessive sudsing, or variations in water temperature. These unforeseen circumstances contribute to the dynamic nature of the time estimate, causing it to fluctuate as the process unfolds.
Further elaborating on the analogy, the post emphasizes the concept of dependencies within both washing machine cycles and software projects. A washing machine must complete specific phases sequentially – filling, washing, rinsing, spinning – just as a software project requires the completion of dependent modules or features before progressing to the next stage. A delay in any one of these phases can have a cascading effect, impacting the overall completion time. The author illustrates this with the example of a delayed rinse cycle affecting the subsequent spin cycle and ultimately delaying the entire laundry process, mirroring how a delayed software module can hold back the entire project.
The blog post concludes by advocating for a more nuanced approach to software estimation, recognizing the inherent complexities and potential for unforeseen delays. It suggests that, instead of relying on fixed, upfront estimates, developers should embrace a more iterative and adaptive approach, acknowledging that estimates are subject to change as the project evolves. Just as a washing machine constantly re-evaluates its remaining time based on real-time feedback, software development estimates should be continuously revisited and adjusted based on progress and any emerging challenges. This approach, the author argues, leads to more realistic expectations and a greater understanding of the true effort required for software development, thereby minimizing the frustration associated with inaccurate predictions.
Summary of Comments ( 412 )
https://news.ycombinator.com/item?id=43085876
Hacker News users generally agreed with the blog post's premise that software estimation is difficult and often inaccurate, likening it to the unpredictable nature of laundry times. Several commenters highlighted the "cone of uncertainty" and how estimates become more accurate closer to completion. Some discussed the value of breaking down tasks into smaller, more manageable pieces to improve estimation. Others pointed out the importance of distinguishing between effort (person-hours) and duration (calendar time), as dependencies and other factors can significantly impact the latter. A few commenters shared their own experiences with inaccurate estimations and the frustration it can cause. Finally, some questioned the analogy itself, arguing that laundry, unlike software development, doesn't involve creativity or problem-solving, making the comparison flawed.
The Hacker News post titled "My washing machine refreshed my thinking on software estimation" (linking to a blog post about how appliance repair informed the author's perspective on software estimation) generated several comments, which largely centered on the complexities and inherent uncertainties of software development compared to physical repairs.
Several commenters agreed with the author's premise, emphasizing the difficulty in foreseeing unforeseen problems in software. One commenter highlighted that software often involves creating something new, whereas appliance repair deals with pre-existing, understood systems. They pointed out the challenge of estimating tasks when the solution isn't fully known, a frequent occurrence in software development. This sentiment was echoed by others who stated that software development involves continuous learning and adjustment, making accurate estimation challenging.
Another commenter drew a parallel between software and home renovation, suggesting both involve uncovering hidden issues that complicate initial estimates. They emphasized the dynamic nature of these projects, where initial plans often change as work progresses.
Some commenters questioned the analogy, arguing that appliance repair, while sometimes unpredictable, is more structured than software development. They suggested that the sheer number of interacting components and the abstract nature of software make comparisons to physical repairs less applicable. One commenter offered an alternative analogy, comparing software development to designing and building a custom house, where unforeseen design changes and complex integrations contribute to estimation difficulties.
A recurring theme was the importance of iterative development and frequent communication with stakeholders in managing expectations and adapting to changing requirements. One commenter mentioned the value of breaking down large tasks into smaller, more manageable chunks to improve estimation accuracy and track progress. Another commenter suggested that the key takeaway isn't about precise estimation, but about embracing the inherent uncertainty of software projects and adapting accordingly.
Finally, some comments touched upon the human element of estimation. One user argued that estimation is inherently flawed due to biases and pressures, suggesting the need for robust processes and communication to mitigate these influences. Another pointed out that estimation often serves more as a negotiation tool than a scientific prediction, reflecting the social dynamics within project management.