So, just why do we want to measure software productivity (without using the root word “productive” in the answer)?  I believe that it comes down to the desire to numerically evaluate an inherently complex process so that quantitative comparisons can be made to provide a basis for decision making:

• Is output per unit of labor or cost increasing or decreasing?
• Benchmarking against “the industry” or “the competition”
• Identify practices that either promote or impede increased output and better quality

I’m sure there are many others that could be added to the list.

Issues

Traditionally, software productivity has been measured as a ratio between units of output and units of effort.  Simple productivity measures worked fairly well for well defined, repetitive manufacturing processes where a 10% increase in input reliably translates to a comparable increase in output, but there are massive problems with applying simple productivity measures to complex, non-repetitive design processes like software development.

Dear Carol: 

I’m a developer in our IT department and we know that project estimating is a big deal for our customers.  Somehow, no matter what we do, we can't seem to get it right.  We do know that project size is an important input to good estimating  and our gut feel is that if we get sizing right, we’ll do better estimates!  I know you recommend using function points, but I’ve also been reading a lot about use case points, story points, SLOC, sizing by analogy, T-shirt sizing, COSMIC and other sizing metrics.  We do a mix of waterfall, agile, iterative and even Kanban to do our projects so what’s the best choice for sizing to get the best results? 

- Size Challenged in Milwaukee

Dear Size Challenged:    

Sometimes I wonder if the internet and the proliferation of (mis)information is a good thing. Before the internet, our choices (for sizing or estimating or anything) were limited and we didn’t have such an overwhelming task to first sift through many options before taking action.  Your list of software sizing choices is an example of this. 

I rather enjoyed the Google Analytics April Fools prank earlier this month, Welcome to Data-Less Decision Making on Analytics Academy.  Though satirical, this video brings to light an important reason why individuals have such trouble making decisions in a business environment: they don’t have data.

I’ll agree that without data it’s really appealing to turn to the coin flip method and be done with it.  After all, 50/50 odds really aren’t terrible, right?  But project management software such as SLIM-Estimate make empirically-based business decisions possible, even when company data isn’t immediately available.

Leveraging our database that contains over 10,000 projects, QSM has developed and regularly updates 17 distinct industry trends.  When creating an estimate or benchmarking a past performance, simply select the QSM industry trend that most closely reflects the type of system being built.  This will serve as a reference point.

If historical data is available but you’re unsure of which metrics to collect, SLIM-SmartSheets is a new downloadable feature in SLIM version 8.2 that mimics the look and feel of SLIM-DataManager and allows users to collect project data, even when they’re not on a network computer.  Each project can then be pulled into one SLIM-DataManager file using the API.  

In a recent, highly-discussed article for Dr. Dobb's, QSM's Carol Dekkers asks a tough question: why are we so woefully poor at estimating software projects? It's a tough pill to swallow considering software developers are among the smartest people on the planet, often boasting advanced degrees in mathematics, engineering, or computer science. Yet study upon study cites that less than one-third of projects are delivered on time or on budget. The problem of software project estimation is not straightforward. To get the heart of the issue, Carol Dekkers takes us through the five top misperceptions about software estimating, and what we can do to address them.

Read the full article on Dr. Dobb's!

The new look and feel of the default workbooks in SLIM Suite are based around infographic and dashboard design principles.  Information Dashboard Design by Stephen Few served as an excellent resource for updating the views and color schemes in SLIM Suite 8.2.  Our goal in updating the look and feel of SLIM-Suite 8.2 was to highlight the pertinent inputs and outputs in bright, bold colors and to allow other view elements like gridlines or historic data to fade into the background by using more muted colors, allowing you to focus on the important metrics when making key management decisions. 

New views in SLIM-Estimate

As you step through the default workbook in SLIM-Estimate, you'll notice that the default folders and view names have changed.  Views have been reorganized into folders with descriptive, functional names to make it easier to find the right views, charts, and reports and give users a more effective dashboard for evaluating solutions, adjusting them and reviewing logged solutions.

SLIM Suite 8.2 comes with four themes to choose from, plus two placeholders for your own custom themes.

White Background (Default)

We know agile works well for small teams and small projects, but monster enterprise projects often require greater capabilities than a small team can provide. So why not scale up agile teams to maintain the cost and efficiency benefits of the agile process while accessing the necessary manpower to pursue complex global projects? On the surface, it makes sense, but what if agile only works when teams and projects stay relatively small? That's the question most CIOs want answered before investing scarce time, energy, or resources chasing the big agile paradigm. In this article recently published on Agile Connection, QSM's Larry Putnam, Jr. turns to cold hard data from completed projects in the QSM database to determine whether big agile is "enterprise savior or oxymoron."

Read the full article!

The problems of software projects are concentrated in three areas: schedule, cost, and quality.  These problems have accompanied software development from the beginning, so they are not new.  Nor have they been ignored.  Huge amounts of thought and effort have been focused on them with unfortunately modest results.  Improvement efforts have been concentrated on management technique (think PMO), process improvement (CMMI, for example), and better tools.  These are all good things, and I can’t imagine embarking on a development activity of any magnitude without them.  However, they have not significantly reduced the incidence of schedule, budget, and quality problems.  Since the problems remain, obviously these remedies have not effectively addressed the root causes of schedule and cost overruns and poor quality.

After enduring the longest winter I can remember, I am eagerly awaiting the arrival of spring, and what better way to do that than to participate in QSM’s annual March Madness Tournament?  For those of you not familiar with our March Madness pool, it’s kind of a big deal.  The top finisher receives a portion of the winnings as well as bragging rights for the following year, and in the process gains immunity from being subject to ridicule by our Commish.  This year the steaks are especially high, as Warren Buffet has offered $1 billion to the person who can guess the perfect bracket.

I will admit that while my standings in last year’s tournament were not as high as I had hoped, I am determined to turn that around this year.  I’ve abandoned my mascot battle strategy and instead will be implementing some techniques used by some of the best software estimators in the field.  I will be using history to determine my picks.  

Instead of spending a lot of time laboriously filling in each line, I had Yahoo Sports auto-fill my bracket based on the teams’ historical standings this season.  To come up with my final score, I averaged the scores of the previous 10 national championship games.  It pains me to predict that some of my favorite teams will be eliminated in the second and third rounds in this bracket, but in the interest of becoming a self-made billionaire I cannot afford to make any emotionally-driven decisions that ignore the stats. 

I think it’s safe to say that nobody really enjoys hearing bad news.  It’s especially hard if you’re the person who has to deliver the bad news, particularly to a superior.  How will your boss react?  Will you be the one held responsible (unfairly) for the project failure?  These are all reasons for keeping the ‘bad news’ to yourself and letting those in charge find out on their own.  

I’ll share a story about one of the first jobs I ever held, as an assistant manager at a summer swimming pool.  My supervisor had a very hands-off approach to management and would often rely on me and the other assistant managers to handle the day-to-day operations of the pool.  Whenever I would deliver less-than-favorable news to him, such as our pool vacuum breaking, or a health inspector dropping by to schedule a visit, my supervisor would literally stick his fingers in his ears and say “La la la la la, I can’t hear you.  Taylor, you know how I feel about bad news.  Fix the problem.”  This put me in a very awkward situation, because as a high school student, I didn’t necessarily have the training or the authority to fix every problem myself, in order to shield him from the ‘bad news.’  

Unfortunately, this type of management exists beyond the pool house and can frequently be found in the corporate world as well.  In an environment where your reputation can mean everything, stakeholders can be very reluctant to receive bad news about the status of their project.  The silver lining in this is that receiving ‘bad news’ isn’t necessarily always a bad thing.  Allow me to explain.