Cloud computing is revolutionizing the way firms host software applications, store and access data, and manage IT infrastructure.  Like any quantum leap in technology, the move to the Cloud brings both opportunities and challenges. A recent public Cloud adoption survey found that only 43% of respondents considered their most recent public Cloud migration an overall success. IT departments moving to the Cloud have a thousand decisions to make. All of them require good data and a solid grasp of business goals, current capabilities, and resources. They also need a simple, practical way to predict future staffing and skills gaps, present options to stakeholders and management, build support, and adjust plans when conditions and priorities change. In this white paper, Kate Armel explains where general purpose estimation tools can help you quickly create and revise estimates, establish repeatable estimation processes, sanity check plans against proven performance, and mitigate known – and hidden - migration risks.

Read the white paper!

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

Software size measures are critical to project estimation, governance, and closeout. Methods for measuring size have been around for decades, yet many organizations still struggle to establish consistent, repeatable processes for capturing software size. Common agreement is that project size is the most important predictor of cost and schedule.

In this PDU-approved webinar, Laura Zuber describes ways to measure software size and gather actual data for completed projects to improve future estimates and in-flight project forecasts.

Watch the replay!

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.

We are pleased to announce the release of the QSM Software Almanac: 2019 Edition, an essential resource for anyone involved in the planning, management, or budgeting of software and systems projects and portfolios. This year's almanac focuses on agile development and the continued relevance and application of estimation and metrics.

The 2019 Almanac presents 18 articles from several perspectives, including both private and public. These articles show that there is indeed a compelling need to apply the basic principles of software estimation to projects, regardless of the methodology used, and that traditional metrics – even sizing metrics – can and should be applied to agile projects. Over the course of this book, the authors examine agile sizing approaches, effort and productivity, estimation best practices, as well as project and portfolio management best practices. All the articles offer research and insights into the foundational skills associated with parametric estimation and adapting those existing skills to account for changing conditions.    

Much of the content in the 2019 QSM Software Almanac is derived from the QSM Metrics Database, drawing data from over 13,000 completed software projects from North and South America, Australia, Europe, Africa, and Asia, representing over 1.2 billion lines of code, 600+ development languages, and 120 million person hours of effort.

We invite you to download the full, complimentary version of the 2019 QSM Almanac below.

At QSM, we have been on the leading edge of software estimation technology for 40 years.  One of our recent innovations is to incorporate machine learning into our SLIM-Suite of estimation and measurement tools.  If you are not familiar, the whole concept of machine learning is to “train” your algorithms with data to improve the accuracy of their predictions.  Simple in concept, but the devil is in the details.  In software project estimation, we are always asked to provide timely decision-making predictions based on skimpy information.  Depending on the situation, our analysis will typically focus on one or several of the following criteria:

1. Schedule (Time to market)
2. Effort (Cost to develop)
3. Staffing and Resources Required
4. Required Reliability at Delivery
5. Minimum-Maximum Capability or Functionality Tradeoffs

We start the training process by utilizing data from completed projects using these five core metrics.  The data usually resides in tools like Jira or PPM products. Once obtained, we run statistical analysis on the data to determine typical behaviors and variability.

Figure 1. Project data used in SLIM Machine Learning Training Process.  Triangles represent completed projects.  Lines are curve fits of the average behavior and statistical variation in the positive and negative directions.  These charts show how time, effort and staffing change depending on the size of the product to be developed.