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NoEstimates panders to mismanagement and developer insecurity

Derek Jones from The Shape of Code

Why do so few software development teams regularly attempt to estimate the duration of the feature/task/functionality they are going to implement?

Developers hate giving estimates; estimating is very hard and estimates are often inaccurate (at a minimum making the estimator feel uncomfortable and worse when management treats an estimate as a quotation). The future is uncertain and estimating provides guidance.

Managers tell me that the fear of losing good developers dissuades them from requiring teams to make estimates. Developers have told them that they would leave a company that required them to regularly make estimates.

For most of the last 70 years, demand for software developers has outstripped supply. Consequently, management has to pay a lot more attention to the views of software developers than the views of those employed in most other roles (at least if they want to keep the good developers, i.e., those who will have no problem finding another job).

It is not difficult for developers to get a general idea of how their salary, working conditions and practices compares with other developers in their field/geographic region. They know that estimating is not a common practice, and unless the economy is in recession, finding a new job that does not require estimation could be straight forward.

Management’s demands for estimates has led to the creation of various methods for calculating proxy estimate values, none of which using time as the unit of measure, e.g., Function points and Story points. These methods break the requirements down into smaller units, and subcomponents from these units are used to calculate a value, e.g., the Function point calculation includes items such as number of user inputs and outputs, and number of files.

How accurate are these proxy values, compared to time estimates?

As always, software engineering data is sparse. One analysis of 149 projects found that Cost approx FunctionPoints^{0.75}, with the variance being similar to that found when time was estimated. An analysis of Function point calculation data found a high degree of consistency in the calculations made by different people (various Function point organizations have certification schemes that require some degree of proficiency to pass).

Managers don’t seem to be interested in comparing estimated Story points against estimated time, preferring instead to track the rate at which Story points are implemented, e.g., velocity, or burndown. There are tiny amounts of data comparing Story points with time and Function points.

The available evidence suggests a relationship connecting Function points to actual time, and that Function points have similar error bounds to time estimates; the lack of data means that Story points are currently just a source of technobabble and number porn for management power-points (send me Story point data to help change this situation).

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Plotting artifacts when the axis involves lines of code

Derek Jones from The Shape of Code

While reading a report from the very late Rome period, the plot below caught my attention (the regression line was not in the original plot). The points follow a general trend, suggesting that when implementing a module, lines of code written per man-hour increases as the size of the module increases (in LOC). There are explanations for such behavior: perhaps module implementation time is mostly think-time that is independent of LOC, or perhaps larger modules contain more lines that can be quickly implemented (code+data).

Then I realised that the pattern of points was generated by a mathematical artifact. Can you spot the artifact?

Module size against LOC-per-hour.

The x-axis shows LOC, and the y-axis shows LOC/man-hour. Just plotting LOC against LOC would produce a row of points along a straight line, and if we treat dividing by man-hours as roughly equivalent to dividing by a random number (which might have some correlation with LOC), the result is points scattered around a line going up to the right.

If LOC-per-hour were constant, the points would form a horizontal line across the plot.

In the below left plot, from a different report (whose axis are function-points, and function-points implemented per month), the author has fitted a line, and it is close to horizontal (suggesting that the mean FP-per-month is constant).

FP against FP-per-month.

In fact the points are essentially random, and the line is a terrible fit (just how terrible is shown by switching the axis and refitting the line, above right; the refitted line should be vertical, but is horizontal. There is no connection between FP and FP-per-month, which is a good thing because the creators of function-points intended this to be true).

What process might generate this random scattering, rather than the trend seen in the first plot? If the implementation time was proportional to both the number of FP and some uniform random component, then the FP/time ratio would have the pattern seen.

The plots below show module size (in LOC) against man-hour (left) and FP against months (right):

Module size against man-hours, and FP against months.

The module-LOC points are all over the place, while the FP points look as-if they are roughly consistent. Perhaps the module-LOC measurements came from a wide variety of sources, and we should not expect a visually pleasant trend.

Plotting LOC against LOC appears in other guises. Perhaps the most common being plotting fault-density against LOC; fault-density is generally calculated as faults/LOC.

Of course the artifacts also occur when plotting other kinds of measurements. Lines of code happens to be a commonly plotted quantity (at least in software engineering).