Estimation

Estimation

Introducing an Estimating Tool to your Process Isn’t all that Scary

Software Estimation Tool

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.   

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Estimation

Webinar Replay: Is Software Estimation Needed When Cost and Schedule Are Fixed?

Fixed Cost and Schedule Estimation Webinar

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.

Watch the replay!

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Agile Webinars Estimation

New Resource: QSM Software Almanac: 2019 Edition

QSM Software Almanac: 2019 Edition

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.

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Articles QSM News QSM Database Agile Estimation

How Machine Learning Algorithms Can Dramatically Improve your Estimation Predictions

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.

Estimation Machine Learning
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. 

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Estimation

The Balancing Point between Project Cost and Schedule

In all production environments, there exists a tension between competing outcomes.  Four variables come to mind:

  • Cost/Effort
  • Schedule
  • Quality
  • Productivity

These do not exist independently of one another.  Emphasizing any one impacts the others.  For example, to compress a project’s schedule, additional staff is typically added which increases the cost.  Larger team size also increases communication complexity within a project which leads to more defects (lower quality).  The development of software  presents a unique issue that may not be present or is at least more muted in manufacturing:  non-linearity.  Key examples of this are the relationships between cost/effort and schedule and the one between schedule and quality. 

Let’s look at some examples.  In the charts below, regression trend lines for schedule and effort vs. size were developed from the QSM software project database.  The darker center lines represent average schedule and effort outcomes as delivered product size grows.  The lighter lines are plus and minus 1 standard deviation.  Roughly 2/3 of the projects in the database fall between the standard deviation lines.  Note the scale on the axes, which is log-log.  This is because the relationship between the amount of software developed and schedule duration or effort is non-linear. 

Software Project Solution
6.5 Month Solution

Software Project Solution
5.85 Month Solution

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Estimation Schedule Effort

Can Estimation & Analytics Improve Vendor Client Relations?

It happens time and time again. Clients look to their vendors to provide software development or configuration services and both sides are often left with big questions. Is the price fair? Can we really get the project done within our duration and resource goals? How can we negotiate for a successful outcome?

There are estimation solutions available that can help. The good ones will leverage empirically-based models, historical data, and industry analytics to uncover which proposals are feasible and which ones are risky.

In the first view below, there are two columns: the “Desired Outcome,” which is one vendor’s proposal and the second column, which is the data-driven “Recommended Estimate.”  The vendor is promising to complete the work in 3 months with a $750,000 price tag. You can see that this proposal is “Risky” and that the vendor will probably finish late and will either have to ask for more money or lose money in the long run.  The charts in the view provide a graphical representation.

Vendor Bid

In the second view for the same project, you see a second vendor’s proposal compared to the “Recommended Estimate.” The vendor’s bid is for 8 months with a $1,000,000 price tag and there is a “Moderately Conservative” rating. In other words, this vendor has a much better chance of achieving what they are promising. 

Vendor Bid

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Vendor Management Estimation

Are Your Software Projects Too Small?

We hear a lot about software projects that are too large or attempt to do too much in too short of a time.  They are very visible and negatively impact both budgets and careers in a not positive manner when they fail.  Small projects may fly under the radar.  This is a mistake.  Most IT projects aren’t large undertakings like Healthcare.gov; rather, they are enhancements and customizations to already existing software systems and account for the majority of most enterprises’ software budget.  Planning these projects to be optimally productive is an area in which most companies can realize the greatest returns.

How do you know what is the optimal amount of software to develop in a project?  In a newly published software benchmark study QSM analyzed productivity, cost/effort, and time to market of a large sample (over 600) of business IT projects that have recently completed.  The projects were divided into quartiles based on the amount of software they developed or customized, which were then compared to each other.  Fully ¼ of the projects were smaller than 3,200 implementation units in size or 68 function points for projects that used that size measure.  Projects in this quartile had a median productivity of 200 IU per staff month or 5 function points per staff month.  The median duration of these projects was slightly more than 3 months. The second quartile contained projects from 3,200 IU up to 8,000 (or 69 to 149 function points).  These projects had a median productivity of 377 IU per staff month (or 7.62 function point per staff month) and lasted a little more than 5 months.  This is a productivity improvement of 89%.  The smaller projects were markedly less productive.  So, simply by bundling software work into larger packages there are significant efficiencies to be gained.

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Estimation Sizing Productivity

Microsoft Services Global Apps CTO Discusses His Team's Evolution Around Estimation

As the Apps Global CTO for Microsoft Services, Lenny Fenster sees the need for estimation in many shapes and sizes throughout the world. In his twenty years at Microsoft, Lenny has also seen many different attempts to improve how Microsoft Services estimates time and effort for software development projects. Not all of them have hit the mark. In this presentation for the QSM 2018 Virtual Conference, Lenny talks about the evolution his team is driving in Microsoft Services to improve the maturity, consistency, and defensibility of software estimation for some of the largest and most complex software projects in the world. He talks specifically about the intentional separation of scope and estimation and the use of SLIM as a key ingredient in the success they are now having. Estimates are now done much quicker, reducing the time to run an estimate from days to just 4.5 hours.  

Lenny was gracious enough to answer questions throughout his presentation about the estimation process at Microsoft Services. This sparked great participation from our audience, who asked a number of questions worth resharing. Here are the highlights:

Q: Did you experience any resistance among the architects in changing the way they did estimation to a new approach?­

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Estimation Webinars

Is Software Estimation Needed When the Cost and Schedule Are Fixed?

In many agile environments, the budget, team size, and schedule are fixed based on an organization’s predetermined targets for sprints or iterations. This leads many project managers to question if software estimation is even necessary. The problem is, without a reliable size estimate, the amount of functionality promised within the time and money constraints could be difficult to achieve and could cause the product delivery to be short on features, or late and over budget.

This is where scope-level estimation tools come into play. They can help evaluate whether targets are reasonable and, even if the schedule and budget are both set in stone, they can help figure out how much work can be delivered. This type of analysis helps set customer expectations and provides data driven leverage for negotiations.

The best estimation tools leverage empirically-based models, industry analytics, and historical data. They can even be used before iteration level planning takes place. They ensure that the overall goals are reasonable before detailed plans are developed. 

In the three views below, we see an estimate generated from a “Time Boxed” method. This is where the product manager was able to input the predetermined time, a productivity measure (PI), and a team size, to see how many story points could be completed within the set constraints. The analysis also includes a “sanity check” of the estimate, comparing it to an agile industry trend from the QSM Industry Database and their own agile historical data.

Time Box

Time Box

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Agile Estimation

Bringing Transparency into Project Contingency Buffers for Schedule and Cost

The application of contingency buffer, more commonly known as “padding” or “management reserve” is the final step in any project estimation process.  The most common practice is for the estimator to use an intuitive multiplier which is added to base estimate.  Unfortunately, everyone has a different multiplier which is shaped by their own personal bias about risk and it is hidden in their head.  This creates a fundamental problem with transparency and consistency within most organizations.

Fortunately, there's a better way.  One solution is to define and configure agreed upon standards that are matched to specific business risk situations.  These should be collaboratively agreed to by all the stake holders in the organization.  Then they can be codified into a configuration that can be selected at the time when contingencies are typically applied to an estimate.  This helps solve the consistency issue.

Project Risk Buffer

To attack the transparency issue, you can use a technique of overlays to visualize the contingency in comparison to the base estimate. 

Project Risk Buffer