Although each workbook is set up with default themes, the look and feel of SLIM-Estimate, SLIM-Control, SLIM-Metrics, and SLIM-MasterPlan workbooks are readily customizable.  

Screen Background

The easiest way to change the feel of your workbooks is to change the background color and style.  To change the background color, go to Tools|Customize Display|Screen/Printer Fonts, Colors, and Symbols…, then go to the Colors & Symbols tab on the right.

Color Start and Color End are important if you want to create a gradient background, like the background in the first image.  A gradient background begins with your specified Color Start color then transforms into your Color Stop color either vertically, horizontally, or diagonally (pictured above).  If you choose the Solid color style, simply select your Color Start.

Graph Background

Like the Screen Background, you can have a solid background or a gradient.  Simply follow the steps above for selecting your colors and styles.

Solutions and Reference Data

Database validation is an important step in ensuring that you have quality data in your historical database.  I've talked before about the importance of collecting project data and what you can do with your own data, but it all hinges on having thoroughly vetted project history.

Although it's nice to have every tab in SLIM-DataManager filled out, we really only need three key pieces of information to calculate PI:

• Size (Function Unit): if the function unit is not SLOC, a gearing factor should be provided (97.3% of projects in the database report total size)
• Phase 3 duration or start and end dates (99.9% of projects in the database report phase 3 duration)
• Phase 3 effort (99.9% of projects in the database report phase 3 effort)

These fields can be thought of as the desired minimum information needed, but even if one is missing, you may not want to delete the project from the database. A project that is missing effort data, for instance, will not have a PI but could be used to query a subset of projects for average duration by size. Likewise, a project with no size will not have a PI, but does contain effort and duration information that could be useful for calculating the average time to market for a division. However, if possible, it is a good idea to fill out at least these three fields.

Version 5.0 of the QSM's Function Point Gearing Factor table is live!

The Function Point Gearing Factor table provides average, median, minimum, and maximum gearing factors for recently completed function point projects. A gearing factor is the average number of basic work units in your chosen function unit. Originally, it was designed to be used as a common reference point for comparing different sizing metrics by mapping them to the smallest sizing unit common to all software projects. QSM recommends that organizations collect both code counts and final function point counts for completed software projects and use this data for estimates. Where there is no completed project data available for estimation, we provide customers with a starting point to help them choose an appropriate gearing factor for their chosen programming language.

For this version of the table, we looked at 2192 recently completed function point projects out of the 10,000+ in QSM's historical database. The sample included 126 different languages, 37 of which had enough data to be included in the table. Interestingly, this year we added three new languages: Brio, Cognos Impromptu Scripts, and Cross Systems Products (CSP).

One trend we noticed is that, in general, the range for gearing factors has decreased over time. Similarly, the average and median values have decreased, which we attribute to having more data to work with.

Read the full press release or visit the new table!

In his book, The Functional Art, Alberto Cairo sets out to explain what data visualizations are, why it is significant to pair data and design, and how to assess whether a data visualization is "good" or not.  In the first chapter, Cairo presents an example from Matt Ridley's book, The Rational Optimist: How Prosperity Evolves.  Ridley asserted that the global population was decreasing over time, using only one line chart.

Cairo  was uncomfortable with that assertion, so he used the UN and World Bank data for fertility rates (the average number of children born to a women in each country) to create a graph that used individual country population data instead of using aggregate data.  The chart below shows all the fertility rates for every country over time.

There are so many stories in the data that it's overwhelming, so Cairo created the following graphic which highlights just a few countries in order to pull out the story within the data:

Having data is great, but if you don't understand how to display it, you can't get your point across.  The focus of this blog series is to explain the various chart types available to you in SLIM-Metrics so that you can efficiently analyze your data, as well as to provide helpful tips and tricks. 

Bar charts break a data set into bins or categories and provide the number/percent of projects or the average metric value for each category.

Unlike scatter plot charts, bar charts can display both numeric and text metrics. There are two metrics tabs on a bar chart property sheet — one for the independent and one for the dependent metric. To create a bar chart, highlight the independent and dependent metric you want to display and select Choose, or simply double-click the desired metric. Once chosen, the selected metric name appears in the field to the right of the Choose button. 

Histograms

Histograms display continuous numeric data (each bar spans the interval between dependent axis ticks) grouped into evenly spaced bins on the independent axis, for the first data set. Additional data sets are overlaid over the bars in a line style with symbols. The Bin Size or Number of Bins can be customized, or you can select Auto to accept the default bin settings.

Histograms show both values and distributions, which is an important way of evaluating single summary statistics, such as averages.  For example, if a PI histogram follows a normal distribution, then you can probably use the average PI for estimation.  If a PI histogram does not follow a normal distribution, then it is a good idea to choose a different method to pick PI.

Effort seems like a metric that's very straightforward, but there is a lot of complexity here, particularly if you are performing benchmark analysis. Recently, I was tapped to help out with a benchmark assessment. One of the metrics that the customer wanted to analyze was effort per function point. "Effort" on its own is very vague, and while the customer might know which phases or activities his organization uses, I can't be sure that definition will match what I think he wants. In order to effectively benchmark, we need to make an apples-to-apples comparison by examining what is really behind the effort number, so it was necessary to send the client phase and activity definitions. 

Here are some helpful definitions to help you understand which activities are included in each phase: 

In a post for The Guardian's Datablog, Jonathan Grey explores the rise of data journalism. Data journalism is "a journalistic process based on analyzing and filtering large data sets for the purpose of creating a new story. Data-driven journalism deals with open data that is freely available online and analyzed with open source tools. 

Although data is a powerful tool, Grey reminds readers that it's not a silver bullet and counters some commonly held data myths. 

Data is not a perfect reflection of the world.