This past July marked the first cyber security recall in automotive history.  Fiat Chrysler issued a formal voluntary recall of 1.4 million vehicles after security researchers Charlie Miller and Chris Valasek demonstrated to WIRED how they could exploit a software vulnerability in Chrysler’s Uconnect dashboard computers and remotely hack into a 2014 Jeep Grand Cherokee over the Internet, taking over dashboard functions, transmission, steering and brakes.  Most notably, they did so from their basement while WIRED author Andy Greenberg was driving the vehicle on the highway!

Though this was first time an automotive manufacturer issued a recall for cyber security, it’s not the first time security risks have been found in automotive software.  As I’ve pointed out in my previous article “How Much Software Is in Your Car?” nearly every vehicle less than 30 years old on the road today depends on lots of computer software and thus is potentially vulnerable to hacking, especially newer models that are connected to the Internet.  

QSM is pleased to announce the release of a new book, Understanding Software Estimation, Negotiation, and Demand Management: An Executive Primer. Historically, only 20% of software projects are completed successfully and with software becoming critical to nearly every company and industry, having such a high rate of failure is simply unacceptable anymore. It is for this reason that QSM has compiled this collection of articles that will aid anyone from project managers to CIOs in implementing software estimation, negotiation and demand management methods efficiently to reduce costs.

Larry Putnam, Sr., founder of QSM and a pioneer and top problem solver in the software estimation and measurement field, provides the foreword to the book, which is co-authored by his son and granddaughter, Doug Putnam and Taylor Putnam-Majarian. Combined, the authors bring more than 40 years of experience in software measurement to a range of topics, including:

Arithmetic mean (aka average) is often a misleading number. One reason for this is that mean is sensitive to outliers. A very large or a very small value can greatly influence the average. In those situations a better measure of center is the median (the 50th percentile). But there is a second huge pitfall awaiting anyone using average for estimating or benchmarking: software size.

Even though we know that software size has a major influence on the key metrics (e.g., effort, duration, productivity, defects) many people insist on reporting and comparing and using the average value. Let’s look at an example. Consider a sample of 45 completed telecommunications application type projects. Picking one of the key metrics already mentioned, duration of phase 3, we can generate a histogram and calculate the mean. The average duration is 27.5 months. Does this tell us anything useful?

The histogram of durations shows a skewed distribution (many projects have a shorter duration, few have a long duration), so we will need to do some sort of normalization before the average is a measure of center.  And even then, what about size?  In a typical SLIM scatterplot of duration versus size for these projects, we can see that in general larger projects take longer than smaller ones.  

A foundation of the SLIM philosophy is to know what your team is capable of producing and never promise to deliver more than those finite limits. Leveraging a history of completed project core metrics enables you to quantify your capabilities, and not only provide a defensible basis of estimation, but support statistical analysis for project benchmarking and identifying performance improvement opportunities.

QSM's newest release of SLIM-Suite, version 9.0, provides a number of enhancements to simplify and organize completed project data in SLIM-DataManager and manage data set definitions and queries in SLIM-Metrics. In this webinar, Laura Zuber demonstrates key new features of both products. You will see how providing project size details, master keyword lists, multiple view layouts, and multi-select variable analysis equip you to expand your historical project repository and increase its use.

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Industry expert Phillip Armour highlights the importance of the QSM Software Almanac: 2014 Research Edition in his recent article for Communications of the ACM.

Software projects can be so complicated and so different from each other that predicting whether they will succeed or fail can be as difficult as forecasting the weather or picking winning stocks. Will the project entirely fulfill its goals? Will it deliver some value at a higher cost or later than desired? Or will it just crash and burn leaving the exhausted survivors to lick their wounds, bury the dead bodies, and shred the evidence?

Courageous efforts are being made to collect and codify the data that is available, to try to spot what trends are occurring in the industry, and to provide some useful guidelines for managing the business of software. The recently published QSM Software Almanac, dubbed the "2014 Research Edition," is an example of this.

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While creating estimates is a fundamental step toward improving productivity on software development projects, it is not enough. In "Full-Circle Estimating," recently published on Projects at Work, Doug Putnam and Taylor Putnam-Majarian present a full-circle model that organizations can apply to track actual performance against estimates, reforecast when significant changes occur, and then continually refine the process through post-mortem assessment.

Doug Putnam is co-CEO for Quantitative Software Management (QSM). He has 35 years of experience in the software measurement field and has been instrumental in the development of the SLIM Suite of software estimation and measurement tools. C. Taylor Putnam-Majarian is a consulting analyst at QSM with over seven years of specialized data analysis, testing and research experience. In addition to providing consulting support in software estimation and benchmarking engagements to clients from both the commercial and government sectors, Taylor has authored numerous publications about Agile development, software estimation and process improvement.

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I have been a software project estimator for 20 years.  Like many people who have worked a long time in their profession, I find myself applying my work experience to other events in my life.  So, when a family member tells me that he or she will be back from a trip into town at 3:30, I look at their past performance (project history) and what they propose to do (project plan) and add an hour.  Usually, I am closer to the mark than they are.

Focused on planning for software projects, this IEEE presentation by Keith Ciocco explains some of the key components of a successful estimation process. This is a summary level view focusing on the importance of leveraging historical data, sizing, and measuring productivity when estimating at the organizational and project level. This presentation includes a demonstration of the SLIM Suite of tools to show how we can automate and streamline the estimation process.

Watch the replay of this presentation!