Listen to the Software Process and Measurement Cast (Here)

The Software Process and Measurement Cast features our interview with Charley Tichenor and Talmon Ben-Cnaan on the Software Non-Functional Assessment Process (SNAP).  SNAP is a standard process for measuring non-functional size.  Both Talmon and Charley are playing an instrumental role in developing and evolving the SNAP process and metric.  SNAP helps developers and leaders to shine a light on non-functional work required for software development and is useful for analyzing, planning and estimating work.

Talmon’s Bio:

Talmon Ben-Cnaan is the chairperson of the International Function Point User Group (IFPUG) committee for Non-Functional Software Sizing (NFSSC) and a Quality Manager at Amdocs. He led the Quality Measurements in his company, was responsible for collecting and analyzing measurements of software development projects and provided reports to senior management, based on those measurements. Talmon was also responsible for implementing Function Points in his organization.

Currently he manages quality operations and test methodology in Amdocs Testing division. The Amdocs Testing division includes more than 2,200 experts, located at more than 30 sites worldwide, and specializing in testing for the Telecommunication Service Providers.

Amdocs is the market leader in the Telecommunications market, with over 22,000 employees, delivering the most advanced business support systems (BSS), operational support systems (OSS), and service delivery to Communications Service Providers in more than 50 countries around the world.

Charley’s Bio:

Charley Tichenor has been a member of the International Function Point Users Group since 1991, and twice certified as a Certified Function Point Specialist.  He is currently a member of the IFPUG Non-functional Sizing Standards Committee, providing data collection and analysis support.  He recently retired from the US government with 32 years’ experience as an Operations Research Analyst, and is currently an Adjunct Professor with Marymount University in Washington, DC, teaching business analytics courses.  He has a BSBA degree from The Ohio State University, an MBA from Virginia Tech, and a Ph.D. in Business from Berne University.

 

Note:  Charley begins the interview with a work required disclaimer but then we SNAP to it … so to speak.

Next

In the next Software Process and Measurement Cast we will feature our essay on product owners.  The role of the product owner is one of the hardest to implement when embracing Agile. However how the role of the product owner is implemented is often a clear determinant of success with Agile.  The ideas in our essay can help you get it right.

We will also have new columns from the Software Sensei, Kim Pries and Jo Ann Sweeney with her Explaining Communication series.

Call to action!

We are in the middle of a re-read of John Kotter’s classic Leading Change on the Software Process and Measurement Blog.  Are you participating in the re-read? Please feel free to jump in and add your thoughts and comments!

After we finish the current re-read will need to decide which book will be next.  We are building a list of the books that have had the most influence on readers of the blog and listeners to the podcast.  Can you answer the question?

What are the two books that have most influenced you career (business, technical or philosophical)?  Send the titles to spamcastinfo@gmail.com.

First, we will compile a list and publish it on the blog.  Second, we will use the list to drive future  “Re-read” Saturdays. Re-read Saturday is an exciting new feature that began on the Software Process and Measurement blog on November 8th.  Feel free to choose you platform; send an email, leave a message on the blog, Facebook or just tweet the list (use hashtag #SPaMCAST)!

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Mastering Software Project Management: Best Practices, Tools and Techniques co-authored by Murali Chematuri and myself and published by J. Ross Publishing. We have received unsolicited reviews like the following: “This book will prove that software projects should not be a tedious process, neither for you or your team.” Support SPaMCAST by buying the book here.

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398982625_e475db57c5_bThe fourth step in our checklist for selecting a size metric is an evaluation of the temporal component. This step focuses your evaluation on answering the question, “Is the metric available when you need it?” When do you need to know how big a project is depends on what you intend to do with the data (that goal thing again). The majority of goals can be viewed as either estimation related (forward view) or measurement related (historical view). Different sizing metrics can be initially applied at different times during a project’s life. For example, Use Case Points can’t be developed until Use Cases are developed, lines of code can’t be counted until you are deep into construction, or at the very earliest, in technical design.

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The major dichotomy is between estimation needs and measurement needs. As Figure 4 suggests, determining size from requirements (or earlier) will require focusing on functional metrics. Functional metrics can be applied earlier in the process (regardless of methodology) because they are based on a higher-level of abstraction that is more closely aligned with the business description of the project. Developing estimates or sizing later in the in the development process opens the possibility of more physical metrics which are more closely aligned with how developers view their work.

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Selecting a software size metric sets you down a specific track.

Deciding on which software size metric you should use is a fairly momentous decision. Much like deciding on a development platform the decision on which size measure will commit an organization to different types of tools, process and techniques. For example the processes and tools needed to count lines of code would be different than those needed to support story points as a sizing technique. The goals of the measurement program will be instrumental in the determining which type of size metrics will be the most useful. Measurement goals will help you choose between four macro attributes of organization specific and industry defined metrics and between physical and logical metrics. For example, if benchmarking against other firms or industry data is required to attain your measurement goal using organizationally defined metrics would be less viable. Similarly if you have a heterogeneous software environment then selecting a functional metric would make more sense than using a physical metric (logical metrics normalizes varied technology).

Figure 1:Balancing Organizational Perspective Versus Organizational Environment

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The second checkbox is whether the measure has an externally defined and documented methodology. Why is definition important? Definition is the precursor to repeatability and consistency, which allows comparability. Consistency and repeatability are prerequisites for the ability to generate data needed to use the scientific method such as Six Sigma and tools used to support Kiazen. Finally, an external definition reduces the amount of effort that is required to construct and implement measurement programs.

Even where a definition exists a wide range of nuances are possible. Examples of the range of definitions begin with the most defined, the functional precision of ISO functional metrics to the less defined methodology of Use Case Points which began with a single academic definition and has evolved into many functional variants. The variants seen in UCP are a reflection of having no central control point to control methods evolution, which we will explore later in this model. The range of formality of definition is captured in Figure 2.

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Figure 3 consolidates the view of formality of definition with the delineation between logical and physical metrics. Each measure has strengths and weaknesses. The first two items in our checklist are macro filters.

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Each measure of size fits a specific combination of organizational goals, environmental constraints and needs however the field of potential software sizing metrics is wide and varied. Once the macro filter is applied each subsequent step in the checklist will narrow the field of potential size measures.

Size matters

Size matters.

All jokes aside, size matters. Size matters because at least intellectually we all recognize that there is a relationship between the size of product and the effort required to build. We might argue over degree of the relationship or whether there are other attributes required to define the relationship, but the point is that size and effort are related. Size is important for estimating project effort, cost and duration. Size also provides us with a platform for topics as varied as scope management (defining scope creep and churn) to benchmarking. In a nutshell, size matters both as an input into the planning and controlling development processes and as a denomination to enable comparison between projects.

Finding the specific measure of software size for your organization is part art and part science. The selection of your size measure must deliver the data need to meet the measurement goal and to fit within the corporate culture (culture includes both people and the methodologies the organization uses). A framework for evaluation would include the following categories:

  • Supports measurement goal
  • Industry recognized
  • Published methodology
  • Useable when needed
  • Accurate
  • Easy enough
Careful, you might come up short.

Careful, you might come up short.

Using a single metric to represent the performance of entire team or organization is like picking a single point in space.  The team can move in an infinite number of directions from that point in their next sprint or project.  If the measure is important to the team we would assume that human nature would tend to push the team to maximize their performance. The opposite would be true if it was not important to the team. Gaming, positive or negative, often occur at the expense of other critical measures.  An example I observed (more than once) was a contract that specifies payment on productivity (output per unit of input) without mechanisms to temper human nature.  In most cases time-to-market and quality where measured, but were not involved in payment.  In each case, productivity was maximized at the expense of quality or time-to-market.  These were unintended consequences of poorly constructed contracts; in my opinion neither side of the contractual equation consequently wanted to compromise quality or time-to-market.

While developing a single metric is an admirable goal, the process of constructing this type of metric will require substantial thought and effort.  Metrics programs that are still in their development period typically cannot afford the time or effort required for developing a single metric (or the loss of organizational capital if they fail). Regardless of where the metrics program is in its development process, I would suggest an approach that develops an index of individual metrics or the use of a balanced scorecard (a group of metrics that show a balanced view of organizational performance developed by Kaplan and Norton in the 1990s – we will tackle this in detail in the future) is more expeditious.  Using a pallet of well know measures and metrics will leverage the basic knowledge and understanding measurement programs and their stakeholders have developed during the development and implementation of individual measures and metrics.

Will one metric make communication easier?

Will a single metric make communication easier?

Measuring software development (inclusive of development, enhancement and support activities) generally requires a pallet of specific measures. Measures typically include productivity, time-to-market, quality, customer satisfaction and budget (the list can go on and on). Making sense of the measures that might be predictive (forecast the future) or reflective (tell us about the past) and may sent seemly conflicting or contradictory messages is difficult. Proponents of a single metrics suggest simplifying the process by developing or adopting a single metric that they believe embodies the performance towards the organizations goals and predicts whether that performance will continue. Can adopting a single metric as a core principle in a metrics program enhance communication and therefore the value of a measurement program?

The primary goal of any metrics program in IT, whether stated or not, is to generate and communicate information.  A metrics program acts as a platform to connect metrics users and data providers. This process of connection is done by collecting, analyzing and communicating information to all of the stakeholders. The IT environment in general and the software development environment specifically is complex. That complexity is often translated in to a wide variety of measures and metrics that are difficult to understand and consume unless you spend your career analyzing the data. Unless you are working for a think tank that level of analysis is generally out of reach which is why managers and measurement professionals have and continue to seek a single number to use to communicate progress and predict the future of their departments.

Development of a single metric that can be easily explained holds great promise as a means for simplifying communication.  A single metric will simplify communication needs if (and it is a big if), a metric can be developed that is easily explainable and is it as useful in predicting performance as most metrics are in reflecting performance.  While there are many elements of good communication such as a simple message, ensuring the communication has few moving parts and is relevant to the receiver are critical.  A simple metric by definition has few moving parts.  The starting point for developing a single metric are the design requirements of simplicity and relevance which can be controlled and tuned (hopefully) by the measurement group as business needs change.

Developing a single metric is a tall order for a metric program, which is why most approaches to this problem use indexes (such as Larry Putnam’s PI). Indices are generally more difficult (albeit there are exceptions, such as the Dow Jones Industrial Average) to understand for wider audiences or fall into the overly academic trap requiring a trained a cadre to generate and interpret them. Regardless of what has been pursued, a single metric done correctly would foster communication and communication is instrumental for generating value and success from a measurement program.

A good number for a birthday but not for a metric!

A good number for a birthday but not for a metric!

In the Lord of the Rings, J.R.R. Tolkien wrote that nine rings of power were created, however a single ring was then fashioned to bind them all.  The goal on many metrics programs is to find the “one ring,” or to create a single metric that will accurately reflect the past, predict the future and track changes.  The creation of a single, easily understood metric that can satisfy all of these needs is the holy grail of all metrics programs. To date the quest for the one metric has been fruitless. However while the quest should continue until both research and testing can be done, adopting a single metric can be dangerous.

A single, understandable metric would have substantial benefits, ranging from the ability to provide an improved communications platform, to a tool to support process improvement activities on areas of the organization where change can make a difference in the metric. An example of a single metric is the Dow Jones Industrial Average (DJIA), which summarizes a large number of individual measures (individual stock prices) into a single easily explainable index. Whether you like or dislike the DJIA most everyone can interpret changes in the index and trends over time. Every daily business program en Market Place (American Public Media, heard on National Public Radio) reports the performance of the DJIA. The problem is when DJIA becomes the only number bereft of context that a problem begins to occur. Often the simplicity has become a narcotic.

Anyone attempting to find a one metric solution (or to use the one metric solutions currently marketed) have a tough hill to climb. There are issues with a one metric solution that must be addressed when designing and developing the solution.  The first of these issues is context. What is important to one organization is different what is important to another and what is important today may not be important tomorrow. How would a single metric morph to reflect these complexities? Lord of the Rings had fewer changes in goals than a typical IT department. A second category of issues ranges is environmental complexity. Complexity includes the interactions between the metric and the human users through the basic mathematical complexity of creating a metric with both the historical and predictive power required.  In my opinion, the most intricate issues swirl around the metrics/human interaction.  In general people will use any measure for wildly divergent purposes ranging providing status to identifying process improvement. Each different use triggers a different behavior.

When seeking a single metric we need to answer the bottom line question is the effort worth the cost. Stated in a less black and white manner, will any single metric be more valuable as a communication tool than the loss of information and transparency that the metric would have?