We live in a multi-dimensional world.  We see in three dimensions.  Our brains’ ability to process spatial relations of height, width, and depth are essential for navigating everywhere we go and every task we perform.  The scientific world has used computers and data to generate 3D graphics for years, because it is the only way to understand complex phenomenon and ideas.  I remember the first 3D color seismic map of rock formations underground I saw.  The image told a story the numbers alone could not.  The business world has caught on in recent years.  The wave began with data warehousing and business intelligence and has grown to include data analytics, artificial intelligence, and machine learning.

QSM has brought a scientific approach to software development project estimation and management for over 40 years.  Our SLIM-Suite of tools contain statistics on the largest repository of completed software projects that enable you to model software projects to predict potential outcomes and assess the reasonableness of your estimates and project goals.  They have always provided four common types of Data Analytics¹ – Descriptive, Diagnostic, Predictive, and Prescriptive.  SLIM tools now provide more in-depth data analytics using quadrant charts.

If you were unable to attend our recent webinar, "Demystifying Cloud Migration Using Estimation," a replay and slides are now available.

Technology organizations spend millions of dollars each year implementing cloud-related programs. Whether the programs are in-house or vendor-related, early planning can be a major stress factor in the management of these programs. Organizations often lose big money and time while trying to carry out cost and schedule targets that were unrealistic to begin with.

Join Kate Armel and Keith Ciocco for this PDU-approved webinar as they show the value of using empirically-based software estimation tools to help mitigate risk and improve early planning and negotiation capabilities when migrating to the Cloud.

Watch the replay!

Technology organizations spend millions of dollars each year implementing cloud-related programs. Whether the programs are in-house or vendor-related, early planning can be a major stress factor in the management of these programs. Organizations often lose big money and time while trying to carry out cost and schedule targets that were unrealistic to begin with.

Join Kate Armel and Keith Ciocco for this PDU-approved webinar on January 22 at 1:00 PM EDT as they show the value of using empirically-based software estimation tools to help mitigate risk and improve early planning and negotiation capabilities when migrating to the Cloud.

Register now!

As we enter a new decade, it's important to take a look back at where we've been. The past 10 years saw the introduction of the Ipad, Microsoft Azure, Uber, 4G networks, and Instagram. Some might look back and call this the era of cloud computing and artificial intelligence with increased connectivity and efficiency in technology like never before. But software development goals have remained largely the same: to reduce cost and time to market, increase quality and maintainability, and allocate resources more efficiently. I'm willing to bet those goals will not change in the coming decade. At QSM, we have always believed that the only way to combat the unknown is with practical measurement and metrics grounded in data from our past. As we look to 2020, let's take a look at our top 5 resources from 2019, featuring the most current insights from our industry experts on the state of software estimation and project management and development trends. 

What better way to get a bird's eye view of software development's history than with a look at long-term trends? Leveraging QSM's industry database of over 13,000+ completed projects, Katie Costantini's "Long Term Trends from 40 Years of Completed Software Project Data" took a high-level look at changes to software schedules, effort/cost, productivity, size, and reliability metrics from 1980 to 2019. The study compared insights to similar studies QSM has completed at regular intervals over the past four decades and answered questions like, 'what is the "typical" project over time?' and 'why are projects "shrinking?"

Many organizations have a need and interest in introducing tools for estimating their IT development efforts, but are reticent due to the perceived disruption, or all out difficulty of execution.  Estimating touches many parts and processes of the organization, so implementation can seem daunting. However, many organizations I work with are surprised by the relative ease of the integration and how the results pay off in spades.

While a process may have been in place for years, or merely months, any good estimating tool should be able to adapt and be woven into that process.  This includes aligning the estimating tool to the nomenclature of the environment and customizing the tool to an organization’s project types such as Cloud, Agile, ERP, Waterfall etc.  Even a seemingly small step of branding the tool itself to the corporate identity, via company logo placement, can help.  Once a tool has your environmental language and “feel” embedded, it starts to belong.  Initially, all of this can be accomplished at a very centralized level – one or two projects with no need for disrupting the work of the project staff.   

If you were unable to attend our recent webinar, a replay is now available.

In many agile and even non-agile development environments, the budget, team size, and schedule are fixed based on an organization’s predetermined targets. This leads many project managers to question how they should proceed with target negotiations and some even wonder if they should estimate at all. The problem is, without a reliable estimate, the amount of functionality promised within the time and money constraints could be difficult to achieve. This could cause the product delivery to be short on features, or late and over budget.

Join Keith Ciocco for this webinar as he demonstrates the role of scope-level estimation tools in evaluating if targets are reasonable and in determining how much functionality can be delivered. This crucial analysis helps set customer expectations and provides data-driven leverage for negotiations.

If you were unable to attend our recent webinar, "Perspective and Predictability in Agile Release Planning," a replay and slides are now available.

Whether you release on-demand or according to a regular schedule, being able to visualize how many sprints it will take provides instant feedback so you can explore a range of schedule, scope, and staffing alternatives. Couple this with the ability to leverage project data - size, cost, schedule, and team performance - and you give your IT planning and decision-making processes a massive upgrade.

Join Laura Zuber for this PDU-approved webinar to learn how SLIM tools employ flexible project models and machine learning to provide information about projects that might not be obvious, enabling you to improve communication among stakeholders across the project lifecycle.

Watch the replay!

Whether you release on-demand or according to a regular schedule, being able to visualize how many sprints it will take provides instant feedback so you can explore a range of schedule, scope, and staffing alternatives. Couple this with the ability to leverage project data - size, cost, schedule, and team performance - and you give your IT planning and decision-making processes a massive upgrade.

Join Laura Zuber on Thursday, Sept. 12 at 1:00 PM EDT for this PDU-approved webinar to learn how SLIM tools employ flexible project models and machine learning to provide information about projects that might not be obvious, enabling you to improve communication among stakeholders across the project lifecycle.

Watch the replay!

Although the software industry is known for growth and change, one thing has remained constant: the struggle to reduce cost, improve time to market, increase quality and maintainability, and allocate resources most efficiently. So how can we combat future challenges in a world where everything is software, from the systems in your car to the thermostat in your home to the small computer in your pocket? By using practical measurement and metrics, we can get a bird's-eye view of where we've been and where we could go, while keeping us grounded in data. Leveraging QSM's industry database of over 13,000+ completed projects, Katie Costantini takes a high-level look at changes to software schedules, effort/cost, productivity, size, and reliability metrics from 1980 to 2019. The current study compares insights to similar studies QSM has completed at regular intervals over the past four decades and answers questions like, 'what is the "typical" project over time?' and 'why are projects "shrinking?"' The results may surprise you!

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It seems like ever since the dawn of software development, humans have struggled with the question of team size. What team size is most productive? Most economical? When does adding more people to a project cease to make sense? So it comes as no surprise that one of the most popular articles on our website is a study Doug Putnam did in 1997 on team size, Team Size Can Be the Key to a Successful Project. The article leveraged data from 491 completed projects in the QSM Database to determine what is the optimal team size - "optimal" being most likely to achieve the highest productivity, the shortest schedule, and the cheapest cost with the least amount of variation in the final outcome. The study determined that for medium-sized (35,000 to 95,000 new or modified source lines of code) systems, smaller teams of 3-7 people were optimal. This article continues to be referenced today, especially by the agile community.

The topic of team size reappeared again in Don's Beckett study of Best in Class and Worst in Class projects for the 2006 QSM Software Almanac. To identify top and bottom performers, he ran regression fits for effort and schedule vs. project size through a sample of nearly 600 medium and high confidence IT projects completed between 2001 and 2004. On average, Best in Class projects delivered 5 times faster and used 15 times less effort than Worst in Class projects. What made the Best in Class projects perform so much better? Best in Class projects used smaller teams (over 4 times smaller, on average) than the worst performers.