If you aren’t yellow, you aren’t pushing hard enough.

Came across great execution: balancing order and chaos blog post by Mike Abbott on uncapitilized.com.  In particular the following quote

If the environment at a startup isn’t crazy, then something’s wrong.

This reminds me of an adage that I use fairly often.

If you aren’t yellow, you aren’t pushing hard enough.

For those unfamiliar with the Red/Yellow/Green (RYG)status colors, otherwise known as the Traffic Light Rating System (apologies for the British-isms in the wikipedia article), which is commonly used in project management to rate the status of a project.  Very quickly, here are some broad guidelines on what RYG means..

GreenProject in great shape, no issues, no concerning risks

Red Project in troubled state, needs help, impactful risks realized, late, overbudget
Yellow Project in okay to not-so-bad state, some issues, some risks, needs care and feeding, might become late, might go over budget
Green Project in great shape, no issues, no meaningful risks, no concerns… All good!

Quite often, project go through their lifecycle going something like the following…

Green, Green, Green, Green, …, Green, Yellow, Red

The optimistic behavior isn’t usually intended, it is human nature to assume that issues are under control, unknowns can be ignored, risks are mitigated and under control.   It’s only at crunch time close to the end when integration is occurring, latent issues or risks are discovered that a project moves to yellow.  Since they collapse on top of each other, the project quickly goes from yellow to red.  Project resets occur, scope is thrown out the window, crap products ship…

Realistically, a good project will ferret out risks, issues, assumptions and dependencies early on in the project.  If after a thorough analysis, the project would generally be yellow.

If the project is still green, the project is a slam dunk.  If you think it’s a slam dunk, then the team can push harder.  Pushing harder brings in some yellow through any of the RAID items above, but they also bring in an element of chaos that Mike talks about in his blog.

Thoughts, comments, counterviews?


Updates on “Getting Good Estimates”

This posting is an update to the Getting Good Estimates article based on the comments received and further research from a number of sources. I include discussion on who should do the estimate, what’s included, references to other estimation techniques, refinements on the probabilistic estimation curve with contrasts to PERT and other techniques.  New discussion is had on the “doubling of estimates”, effort vs calendar time,  the funnel of uncertainty and finally thoughts on the experience level for shaping estimates against the engineer’s experience.

UPDATE: Improved approach to estimation in this posting.

Who Does the Estimate?

The first comment I saw was from logjam over at hacker news who posted the following (emphasis is mine):

When managers request software estimates from engineers, engineers should frown, look them dead in the eyes, and tell them that making estimates is a managerial/administrative task.

Interestingly is the polar opposite to Joel on Software

Only the programmer doing the work can create the estimate. Any system where management writes a schedule and hands it off to programmers is doomed to fail. Only the programmer who is going to implement a feature can figure out what steps they will need to take to implement that feature.

I tend to agree with Joel on this one.  The person solving the problem is in the best position to determine how long a particular task will take.  Moving away from software, people get very frustrated with cookie-cutter estimates from tradespeople, independent of the actual effort associated with the problem.

The estimate is not a negotiated value between the engineer and the manager, it is instead a shared consensus on the effort of the task.  The engineer’s responsibility is to integrate the assumptions, risks and their individual capability into an estimate.  The manager’s responsibility is to provide the bigger picture to provide the information needed for the engineer to integrate, and the commitment to

Another point that was brought up by my friend and colleague, Piranavan, is that estimates should be tempered by the individual’s strengths and experience.  An architect who knows the system through and through will usually be able to deliver a task within a considerably shorter time than an intern that is new to a system.  This underscores what Joel and I mention above.  The estimate should really come from the person doing the work.  It can be workable to have experienced engineers create estimates, but before the estimates become plans of record, the person making the estimate needs to temper the estimate with the individual doing the work.

What’s Included in an Estimate?

Immediately below the statement from Joel on Software, there was the following statement.

Fix bugs as you find them, and charge the time back to the original task. You can’t schedule a single bug fix in advance, because you don’t know what bugs you’re going to have. When bugs are found in new code, charge the time to the original task that you implemented incorrectly. This will help EBS predict the time it takes to get fully debugged code, not just working code.

This concurs with what I look for in estimates.  Completed work, done… Done, done…  Hands off keyboard, done…  Delivered with minimal bugs,  done…

I look the engineer dead in the eye, ask them to put their hand on their heart confirm that their estimate includes all work that’s needed for the task to be complete.  Most engineers will pause and possibly realize that there is other work or unconsidered risks that might affect the estimate.

The intent isn’t to beat the engineer up, the intent is to dig down and expose any assumptions, concerns or other issues that may affect the estimate.  Remember that the captured form of the estimate is either an explicit range or a single effort value with confidence interval applied

What Other Estimation Techniques Are There?

There are obviously many different techniques that can be used for estimations.  A google query for “Software Estimation” yields 31,400,000 results. 10 pages worth of results in,

There are many, many different methods.  Here are a couple of interesting and accessible ones.

Planning Poker is a group consensus system.  There is a group discussion on the details regarding the task, and then everybody creates their estimates.  These are then combined to determine a group estimate.

Evidence Based Scheduling by Joel Spolsky in 2007 predicates estimations down to less than 16 hours.  This forces a level of design as part of the estimate.  The estimates are not trusted until they get down to that timeframe.  I’d imagine that the estimate/design is revised and improved over time.  Jump down to the uncertainty funnel below for a discussion there.

Probabilistic Evaluation and Review Technique (or PERT) for short, provides a full system for estimation.  The methodology also takes the probabilistic estimation curve and boils it down to 3 points on the curve: the optimistic, most likely and pessimistic.  These are then calculated into a single estimate as shown below.

Probabilistic Estimation Curve

One of the key parts of the previous post presented the characteristic curve. As part of the research for this update post, I saw the curve in multiple places from papers on terminology to more NASA handbooks on estimation. The research provided a lot more nuance to estimation.  It is also referenced heavily as the basis for the 3 point estimation technique used in PERT.

Although I haven’t confirmed, I believe that the probability function that closely matches this shape of a particular beta distribution

Refreshing with the graph.

Notice that I’ve marked the three critical parts.

Absolute Earliest The absolutely earliest that the task could be completed. This is assuming perfect understanding of the task and no unrealized risks.  Basically the impossible estimate.  Way too many estimates are based on this value.
Highest Confidence (engineer’s estimate) This represents the highest confidence, and the likely point at which the task will be completed.
Mean (planning estimate) This represents the mean of the estimate.  I’ll dig deeper into this shortly.

PERT provides a basis for determining the estimate based on the formula

Estimate = Mean = (optimistic + 4*Likely + pessimistic)/6.

This of course assumes that the least likely estimate is captured as a number, which in a lot of cases is quite hard to do.

Padding Estimates

If you’ve been in software engineering for a while, you probably have heard someone say “Take the estimate and double it”.   The paper by Grimstad et al actually positions this in context.  They make a similar explicit observation that the estimates for any task have a probabilistic shape with the two critical points.  The highest confidence and the mean.

These two points carry particular value and should be used in two different scenarios.  The highest confidence should be used by the engineering in tempering and improving their estimation.  The mean should be used within the project management team to determine a likely cost or planned effort for the project.   Both are rooted in the same estimation but are derived differently.

The estimation doubling is triggered by a gross simplification of the estimation process.  Simplifying the estimation to a single scalar value from a probabilistic range makes it easier to aggregate numbers, however the aggregation should be the mean rather than the highest confidence.  If you conflate the two values together you will end up with poor overall planned effort.  Remember that the engineers will optimistically provide something between the absolute earliest and their highest confidence estimate so this is generally the number used as a scalar estimate and hence as the basis for estimate doubling.

Since there is the tendency to use the highest confidence estimate as a basis for planning and these estimates will be typically be lower than the planned effort we end up with a shortfall.  To recover from this shortfall, the simplest model is to use an arbitrary multiple.  Falling back to our probabilistic model, we see that the mean is a non-linear distance from the highest confidence estimate.  The management of risks and unknowns shape the confidence associated with a task.

A well understood task may have a small difference between the absolute minimum, the highest confidence and the mean, a poorly understood task will have a greater spread.  The size of the task (or the absolute minimum) carries no direct relationship to the spread of the estimates.

This removes the “double the estimate” for the purpose of planning.  The use of the more nuanced mean or planning estimate should be used instead.  Or put differently, the factor by which the engineers estimate is transformed into the planning estimate is proportional to the level of risk and number of unknowns.  The higher the level of understanding of the risks and issues for a task, the lower the multiple should be.

Of course if this means that doubling the estimate may make sense in some environments, particularly when the estimate carries a lot of unknowns and is known to be  optimistic or  has not been tempered by the sorts of discussions suggested in the original article.

The Uncertainty Funnel

An implication of the shaping and discovery process I described earlier is that over time the estimates become more accurate as more information is discovered and as the project continues.

A number of papers show this in different forms.  Page 7 of the NASA Handbook of Software Estimation shows a stylized funnel, and page 46 of Applied Software Project Management (physically page 15 in the Chapter 3 PDF referenced) book shows an iterative convergence of estimations in its discussion of the Delphi Estimation model.

Both these reference re-iterate that estimates are not static.  Estimates should be revisited and re-validated at multiple  stages within a project.  New information, changes in assumptions and changes in risk profiles will shape the estimate overtime.  I’d also suggest that the engineers quickly do a sanity check on the estimates they are working against before starting work on a new task.  The estimate will generally improve over time as the risk discovery, problem understanding and task detail awareness increase the accuracy of the estimate.

Visionary Tools provides an interesting observation that if you don’t see estimate uncertainty reducing over time, it is likely that the task itself is not fully understood.

Interdependencies & Effort vs Wall Time.

Piranavan highlighted another area that I had left ambiguous.  The discussion on estimates is focused on looking at the particular effort associated with a singular task.  It does not expand into managing interdependencies and their effect on meeting an estimate.  For the purposes of an estimate in these articles, it is the time applied to delivering the estimate.  The external factors such as interdependencies, reprioritization, etc should not affect the value of the estimate. Here I do see the prime value of the manager being in the running defence for the engineer and ensuring that they have the capability and focus to successfully deliver the work with minimal interruption or distraction.  This may mean delaying the delivery of the work, or assigning other work elsewhere.  Always remember that sometimes you need to take the pills and accept late deliver.

A further subtlety on the interdependencies is that if an interdependent task is not well-defined or delivered cleanly or completely, that may force rework or rescoping of tasks and consequently it is likely that these external factors will inject bumps into the uncertainty funnel.

On Feedback Cycles and Historical References

logjam made the following point:

Managers should collect, maintain, and have access to substantial historical data upon which they can make estimates and other administrative trivia. What else are managers for? Of course, what engineers need to understand is the game at work here: making an estimate is primarily about making you commit to a date, with which you will be flogged by those asking for such an estimate.

Piranavan wrote

I also think that estimating work is something that needs to be adopted in a weekly cycle. Capturing the changing estimates are important to understand that things are changing and need to be accounted for as well as a strong feedback tool for engineers to understand where previous estimates went awry (estimates vs actual). It also gives managers a chance to understand how close engineers were and whether or not that was an estimation error or an outlier (external priority change for example).

Whilst I don’t agree with logjam’s assertion regarding managers making estimations in isolation of the engineers, both of the responding comments point out the need to capture, manage and maintain estimates throughout the life of a project, and if possible educate the engineer on how to improve the accuracy of their estimates.  That’s a topic for a later article.

Comments, suggestions or pointers are welcome below.

Getting Good Estimates

Good estimates are hard to come by.  They are typically too optimistic or too pessimistic or aren’t grounded in reality.  Here is my approach to effort estimation.  I’ve used it successfully in a number of roles and have seen engineers go from poor to reasonable to good estimators.

UPDATE: I have gathered some thoughts and comments and included them in this update.

UPDATE 2: I have an update on the methodology and some further insights in this blog post.

What I look for in Estimates

Typically when asked for an estimate, you will get a single value with no qualification.  “The work will take 3 weeks”.  Experience has shown me that the a single value implies a lack of understanding the nuance of the problems and issues that the task might have lurking just below the surface.

When asking for an estimate, I’m looking for two things.  1) A baseline effort, and 2) A confidence interval.  This comes in one of two forms

  1. 4 weeks of effort with 60-70% confidence
  2. 3-6 weeks of effort

Both these values are effectively the same.  I let the engineers choose which ever one they are comfortable with.

Characteristic Curve

I can’t recall when I began to understand the characteristic curve within the methodology I use for engineering. I’d say that a long-term colleague Larry Bonfada was a strong influence in the thought process and I have since seen similar characteristic curves in Waltzing with Bears: Managing Risk on Software Projects by Tom DeMarco and Timothy Lister.  I don’t have sufficient a background in statistics to define the shape.  Feel free to leave a comment to educate me on the distribution type.

The critical sections of the curve in the table below.




Absolute Earliest

The absolute earliest date that the task can be complete.


Highest Confidence

The date that represents the highest likelihood of being delivered on or around.


Long Tail

Worst case scenarios, if things go wrong, this date will be hit.


Typically engineers will choose one of those sections for their estimates.  Optimists will communicate the absolute earliest date, pessimists will go for the long tail and your more experienced realists will go for the point of highest confidence – somewhere in the middle.

Shaping the Curve

Quite possibly you are thinking that to get this curve you have to apply painful or difficult to use models; fortunately, it’s not rocket science.  Most engineers actually have a strong gut feel for the shape of the curve, so it’s a matter of teasing out a good estimate.

The way it works is through a set of questions to the person providing the estimate.



What’s the lowest effort for this task?

 2 weeks

What’s the likelihood it will task 20 weeks


What’s the likelihood it will take 10 weeks


What’s the likelihood it will take 5 weeks


What’s the likelihood it will take 4 weeks


What’s the likelihood it will take 3 weeks


I intentionally use an number of extreme points (10,20 weeks) to drive the shape of the curve.  When graphed, it comes up similar to below.

I find that most engineers will naturally have a strong gut feel for the estimates and in the majority of times will give numbers that result in more or less the same shape.

Now of course, there are a class of engineers who are either so cautious that they always estimate in the long tail – or too optimistic (or naive to the real effort) that they will always resist this sort of analysis.  My advice is to push through with them (or at least work out a way to interpret their estimates).

From the answers to the questions in the examples above, I’d walk away with either of the agreed to estimates of 2½ – 4 weeks of effort or 3 weeks with 60-70% confidence.   Each team or organization will have it’s own sweet spot of acceptable range.  Tightening and getting the estimates to the right shape, usually involves a mixture of analytics and soft management skills.

Tightening the Curve by Managing Unknowns

The uncertainty in the curve is representative of a number of different factors, be it experience, unknown complexity, inter-dependencies and so on.

A hallmark of a large amount of unknowns in this sort of analysis is overtly broad ranges.  I’ve had engineers give a range of 2 weeks to 3 months.  Obviously the estimate isn’t workable by any stretch of the imagination.  The engineer in this case is either being obstructionist or hasn’t, or isn’t willing, to look at the unknowns that would drive such a broad estimate range.

The types of questions that I tend to ask the person giving the estimate will be along the lines of

  1. What could happen that will prevent the absolute earliest time from occurring?
  2. What could happen that would push you from the 60% confidence date to a later date?

As each of these questions are answered with the unknown factors becoming more visible, you can revisit the original estimating questions again after those factors have been determined.  If you are lucky there are some factors that are either issues that can be dealt with or risks that can be mitigated or removed.  In addition, it is worthwhile and discover these issues and risks and have them tracked formally as part of the greater project.

I generally find repeated cycles of this sort of analysis serve to improve the estimates to the point where I am comfortable to accept the estimate into the project.  With each iteration the discovery process either moves the overall curve to the left or the right (smaller or large effort) or tightens the shape of the curve (increasing the confidence).

Feel free to provide feedback below on how you deal with estimates.

All Projects Great and Small

Projects come in all shapes and sizes. However there is a human tendency to begin to look for consistency in the way projects are run. If there is the possibility to make all the projects look the same and be approximately the same size, then we’ll force them to the same size. In my particular domain, software projects will aggregate sub projects until they get to a particular size that warrants an organization’s standard project methodology.  This has a number of possibly unintended consequences that bear consideration.
Continue reading “All Projects Great and Small”

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