QSM Database

QSM Database

What Our Major QSM Database Update Means to the Software and IT Community

QSM Database Update

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QSM recently announced a major update to the QSM Software Project Database, a large and robust body of data that helps software and IT professionals estimate the cost, time and effort requirements for their software and systems projects. As a result, QSM clients and SLIM Suite users can benefit from the most up-to-date and expansive software project benchmarking data, particularly in the agile domain.

With this large update, we’ve validated and added more than 2,500 new projects across nine major application domains (Avionics, IT, Command & Control, Microcode, Process Control, Real Time, Scientific, System Software, and Telecom) and 45 sub-domains. The result is a database with more than 13,000 completed projects, extending what is already the largest continuously updated software project metrics database in the world.

With these enhanced data insights -- all gathered from real-world projects -- SLIM Suite users have access to the most up-to-date software project benchmarking data and can quickly and easily sanity-check estimates against industry data.

IT and Agile Projects Get a Boost

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QSM Database Agile

QSM Database Now Includes More Than 13,000 Completed Projects

QSM is pleased to announce a major update to the QSM Database, the largest continuously-updated software project performance metrics database in the world. With this update, we have validated and added more than 2,500 projects to the database in 9 major application domains (Avionics, IT, Command & Control, Microcode, Process Control, Real Time, Scientific, System Software, and Telecom) and 45 sub-domains, resulting in a current total of more than 13,000 completed projects.

With this update, the number of agile projects in the database increased by 340%, resulting in some changes to the agile trend line. Specifically:

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QSM Database Metrics SLIM Suite

The 2017 Software Almanac: Development Research Series

QSM Software Almanac: 2017 Edition

Software plays an increasingly vital role in our everyday lives. It powers everything from autonomous cars and aircraft, life-saving medical equipment, and the data that allows the government to protect our country. When companies develop software, there’s no room for error. 

That’s why software predictive analysis and estimation are still extremely important. Last year, with the release of the 2016 Software Almanac, we learned that the last 35 years of predictive analytics and estimation principles were still incredibly relevant for providing reliable and applicable business intelligence for implementing successful software projects.

This year’s version of QSM’s annual Software Almanac further strengthens those findings. The 2017 Software Almanac builds on the principles identified in last year’s publication and highlights the dangers of not applying predictive analysis and estimation processes.   As stated by Angela Maria Lungu, Almanac Editor and Managing Director at QSM, these principles can be a “double-edged rearview mirror.” If you move forward without applying the historical principles of estimation and analysis correctly, their value is diminished.   Here’s what else you can expect from this year’s Almanac:

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Articles QSM Database

Top Programming Languages Revisited

Mike Harris at the Davis Consulting Group blog links to a 2014 list of 11 Essential Programming Languages from Baseline Magazine:

If you want to learn about the hottest programming languages today, don't miss this list from IEEE Spectrum. This respected organization, which has 400,000 members and is considered the world's largest association of technology professionals, enlisted the services of Nick Diakopoulos, a well-known computational journalist and assistant professor at the University of Maryland, to compile the language rankings. Diakopoulos proceeded by weighing and combining 12 metrics from 10 sources, including IEEE Xplore, Google and GitHub. The result is a compilation of languages that cover big data analytics, graphics, system administration, network programming and virtually every other tech-supported function.

IEEE’s interactive list, which you can explore here, generates customized rankings for various sectors (Web, embedded, enterprise). In evaluating the results, it makes sense to ask, “What makes a programming language, ‘essential’?” Language popularity can be measured several ways:

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Languages QSM Database

The "Typical Software Project" Over Time

What does a typical software project in the QSM historical database look like, and how has “what’s typical” changed over time? To find out, we segmented our IT sample by decade and looked at the average schedule, effort, team size, new and modified code delivered, productivity, language, and target industry for each 10 year time period.

The QSM benchmark database represents:

  • 8,000+ Business projects completed and put into production since 1980.
  • Over 600 million total source lines of code (SLOC).
  • 2.6 million total function points.
  • Over 100 million person hours of effort.
  • 600+ programming languages.

During the 1980s, the typical software project in our database delivered 154% more new and modified code, took 52% longer, and used 58% more effort than today’s projects.   The table below captures these changes:

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Team Size Languages QSM Database Effort

Data-Less Decision Making

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.  


A Year in Review

As 2013 begins to wind down and everyone begins making plans for 2014, I wanted to take a moment to reflect on all the projects we’ve worked on this year.  Despite our relatively small company size, we’ve managed to accomplish quite a bit over the last year.  Below, I’ll recap everything we’ve been up to and also highlight some of our great resources and publications in case you missed them earlier:

QSM Announces Latest Update to the QSM Project Database

We are pleased to announce the the latest update to the QSM Project Database! The 8th edition of this database includes more than 10,000 completed real-time, engineering and IT projects from 19 different industry sectors.

The QSM Database is the cornerstone of our business. We leverage this project intelligence to keep our products current with the latest tools and methods, to support our consulting services, to inform our customers as they move into new areas, and to develop better predictive algorithms. It ensures that the SLIM Suite of tools is providing customers with the best intelligence to identify and mitigate risk and efficiently estimate project scope, leading to projects that are delivered on-time and on-budget. In addition, the database supports our benchmarking services, allowing QSM clients to quickly see how they compare with the latest industry trends.

To learn more about the project data included in our latest update, visit the QSM Database page.

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QSM Database

Agile Series Part 1: The "Typical" Agile Project

After spending the past few weeks working with the Agile projects in QSM’s historical database, I’ve become interested in Agile Development Theory, particularly due to its popularity. While spending days at a time examining our database, I’m left with numerous data-driven questions. Therefore, I thought I would take this opportunity to write a series of Agile-related blog posts.

QSM’s database contains over 100 Agile projects from the U.S. and abroad. The projects include a variety of application types and their top three programming languages were JAVA, C++, and VB.NET.  Seeing this, I thought it might be interesting to examine the “typical” Agile project according to our data.

So what does the “typical” Agile project look like? For consistency purposes, I limited the sample to IT systems projects completed in the last six years. I measured the Duration, Effort, Average Staff, and MTTD at various project sizes to see how they compare.

Below are two figures that give demographic information about our “typical” Agile projects: 

Typical Agile Project

This scatter plot shows the individual Agile projects compared against QSM’s Business Agile trends.

Size (SLOC)

Duration (Months)

Effort (PHR)

Average Staff

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Agile QSM Database

Top 25 Programming Languages since 2008

Top 25 Programming Languages since 2008

In response to my previous post, I made a new word cloud for the top 25 programming languages in the QSM historical database from 2008 to present.

One striking difference between this word cloud and the last week's is that the font sizes are much smaller, due to the smaller sample size. Since word clouds use font size to represent size within a sample, this is expected since the entire QSM database is larger than the sample from 2008 to present. 

Unlike last week's cloud, Java is the predominant programming language since 2008. Java represents 26% of the sample since 2008 while COBOL, the #1 programming language in the entire database, holds only 11% of this sample. According to Langpop.com, a site which ranks the popularity of programming languages using search results, Java ranks second in the Normalized Comparison chart, just below C.  

In Programming Language Trends: An Empirical Study, a paper from the New Jersey Institute of Technology, the authors attempt to predict the popularity of programming languages by using regression analysis which focuses on intrinsic and extrinsic factors.  

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Languages QSM Database