Estimates are “mission-critical” for Software Product companies because:

• In the short term, the quality of the development estimate can win or lose a deal
• In the medium term, on successful deals, the quality of the development estimate can drive profit or loss on a deal
• In the long-term, the quality of the estimates drives the evolution of the products – not just what we say we will do in the road maps but what we actually deliver in releases.

Phil Laplante of Penn State is a prolific and respected writer on many software and project topics as well as being part of my local IT community.  In the July/August issue of the IEEE IT professional magazine, Phil steps back and writes a thoughtful and though provoking article that is perfect for contemplation on the beach.

In “Nexialism and the Law of Unintended Consequences,” (there’s a search term you won’t use very often) Phil suggests that to understand complex interactions that could impact our project risk we need to spend at least some time thinking in simple terms about the big picture.  Nexialism is defined as “the science of joining in an orderly fashion the knowledge of one field of learning with that of other fields.”

OK so that sounds a little grandiose and you might be wondering how it impacts your project risks.  Think of it this way, how many times in a failing project did somebody know that it was going wrong early but was never asked?  Is the test manager communicating (talking and listening) with the coding team lead (and vice versa).  What is the opinion of the DBA’s?  The performance test team?  Like it or not, these are different disciplines (or fields if you like) with different perspectives and, too often, we do not take care to “join their knowledge in an orderly fashion.”

Keith Baxter of De-RISK, based in the UK, has developed a simple but effective way of capturing and visualizing the impact of risk on a project estimate.  The approach, called Quality Based Costing (QBC),  and some examples are provided in the following paper on Keith’s website:

Quality Based Costing Paper

QBC is based on the responses to structured questions about each project component which seek to identify four costs for that component:

• Absolute Minimum
• Best guess/realistic estimate
• Contingency added
• Disaster scenario

The approach is then to apply Monte Carlo simulation to derive a probability distribution of the likely cost.  I particularly like an estimation process that includes Monte Carlo simulation because it forces the project manager and organization to make an informed choice about the risk they wish to take on each project.

The May 19, 2010 edition of Computerworld magazine contained a couple of articles about the growing evidence that one of the reactions to tightening budgets in software development has been the increased use of open source software and the associated  “Hidden Snags”

It is clear that one of the attractions of open source is the availability of source code which is critical for integrating open source into the enterprise but this brings problems of its own (“Integration Jams”).  I have written before about the benefits of static code analysis and I really want to stress the value of getting a one-off analysis done of any significant open source code that you bring into your enterprise.  Such a one-off analysis can be delivered as a service and does not require purchasing or learning a new tool.

The SEER estimate by Comparison capability is something that I talk about a lot to company’s who are really struggling because their SWAG approach to estimating projects is neither repeatable nor coherent.  Often, the projects they are failing miserably to estimate professionally are mission-critical either for deliverables to customers or for internal investment (budget) planning.

At some point, these estimate amateurs may need to base their estimates on a repeatable sizing approach (of which Function Points is just one option) but they need to change their ways quickly so I often talk to them about doing estimates by comparison using SEER.

Even if you don’t see yourself buying a tool right now, I recommend investigating this approach so you can attend in real time or review later.  The technique is powerful even if the tool is not right for you.  DCG can also provide the tool as a Service so you can pilot the application or just use it a few times every year for this really important estimates.  For more information on this service, you can contact David Herron at d.herron@davidconsultinggroup.com.

The Estimate by Comparison application has traditionally been used to empower users to develop an understanding of software size; the single most significant driver of development cost, effort, and schedule for software projects.  However, SEER Estimate by Comparison has evolved and can be utilized with all of the SEER solutions to provide an insight into effective definition of scope through a series of project analogies and/or comparisons to a user’s repository of past projects, thus helping users to develop a reliable estimate on a project’s scope even when information is scarce.

SEER Estimate by Comparison further adds capability to the project team when used in a more contemporary manner. It can be manipulated in such a manner that a wide realm of subjective, qualitative alternatives can be evaluated in context of the project as a whole in a robust, repeatable and ultimately measureable manner.

Software development for software products shares all of the opportunities and challenges of software development in corporations with a few majors quirks all of its own.  One of these is the issue of managing a code line for a single product while accommodating (or not) variations for specific customers.  If you are working or have worked for a software product developer, you will be familiar with this problem.

Essentially, the easiest, cheapest and most efficient way to maintain a software product is to have a single code line with all clients on the same, latest version.  In this ideal model, any defects can be fixed in the single code base and provided to clients in the next maintenance release (maybe with a short-term patch to get over any short-term delay).  This model is illustrate below:

At the other extreme, different clients for a software product each have different versions of the software in production with differing levels of maintenance releases and patched loaded.  Many of the clients have their own special customizations to the code some of which overlap with the core product code. A simple example of the challenges is illustrated below:

Many software products will acknowledge more complicated scenarios than this.  Over the years, managing these scenarios has been made slightly easier by more sophisticated configuration management software which manages code branches and code line re-mergers.  That said, such systems still require a level of discipline comparable to the complexity they offer to manage and it is crucial to remember that some code branches just cannot be “automatically” re-merged.

If it is so difficult to manage code branches, why do software product vendors allow or even facilitate the branching of the core product code line?  The following drivers are all relevant:

1. New software product releases of any kind require effort from the clients to acceptance test before loading into  production.
2. A big license deal requires some updates to the product that are needed before the next release will be ready so the software product vendor creates a custom version for the “special”client.  When the next release comes out, the client is in the middle of configuration and implementation and does not want to take the new release.
3. There is pressure on software product vendors to provide minimal patch fixes to a clients specific problems to avoid the need to load maintenance releases which fix every clients problems.
4. All clients believe that they are unique and need changes to software products to reflect their specific operating methods (instead of changing their operating methods to suit the software).
5. All clients understand the long-term costs of customizing software products and so strive to stay on the core product line without necessarily loading every release (Why load a new release if WE don’t need the new functionality or bug fixes?).

In the April 26, 2010 print edition of Informationweek, the Dr Dobb’s Report contained a an article by Sid Sidner of ACI Worldwide describing his teams choice and implementation of a Static Code Analysis tool.  ACI looked at:

• AdaCore’s CodePeer
• Coverity’s Prevent
• GrammaTech’s CodeSonar
• Greenhill’s DoubleCheck
• Klocwork’s Insight
• Lattix’s LDM
• Microsoft’s StyleCop
• Ounce Labs’ Ounce Core

You can read Sid’s comments on each of these tools in his article.

The article is a very good description of how to integrate a software analysis tool into the development environment. Some engineering shops like ACI are a little bit ahead of IT organizations in building quality and security into their products.

Sid offers the following “Tips for Success”:

• Define an “initial issue” policy - what will you do with the code issues that identified by the first analysis run?
• Get the global mechanics working - many of the tools require license managers and centralized result servers
• Attack one product at a time
• Identify SMEs - those product experts who will also be experts in the tool
• Train the SMEs
• Work with the SMEs
• Train the developers
• Perform initial analysis on existing code and defer all issues - this is Sid’s suggested choice of “Initial Issue” policy.  Note that he suggested to defer not ignore.
• Deliver help from SME’s to developers as required
• Run the build anlaysis often
• Review deferred issues

I discussed this article with Lev Lesokin of CAST Software who have their own static analysis application, the Application Intelligence Platform (AIP).  Lev highlighted a couple of considerations which Sid’s paper did not cover:

1. ACI probably do a very good job building quality into their code at a module level, but they should also look at quality at a broad architecture level. Interactions between modules, between distributed components, and most importantly between the database and the code base. Some of the most risky quality issues actually reside in that application level and they are the hardest to identify by code walk-throughs or localized static analysis.
2. When analyzing your software, there’s an opportunity to also use that to introduce measurement. Sid could put some metrics in place to make sure his teams are getting better at building quality and security in. Also, risk metrics can be used to make go/no-go decisions at stage gates. This might be a point for his VP of Engineering, though. He/she can introduce productivity measures and drive the organization towards continuous improvement.

These two uses of software analysis and measurement products might not be as relevant for ACI, because much of the software they build is monolithic products that they ship and don’t see in a distributed environment until they implement at a customer. In an IT organization you will have many moving parts, connected through a services layer or various other interfaces. This is where app-level quality issues become much more present. Also, getting apples-to-apples measures across development teams in Java, .NET, DB stored procs, etc., is much more challenging. Once ACI’s customers integrate their newly acquired payments system into their bank’s accounts and cash management systems, they might consider an architecture-level analysis to determine how structurally strong their environment is in terms of stability, performance and security.

“AIE” is the last in the series of four metrics for measuring the business value of IT that I introduced in my posting of July 7, 2009.  The first three in the series were the “Business Value Index,” “Total Economic Impact” (TEI) and “Val IT.”

AIE is considered to be the most rigorous of these four approaches but it has not gained widespread use.  With its mathematical, statistical and economic underpinnings, AIE provides investment decision-makers with a high degree of confidence in its results but there is a steep learning curve and it requires significant expertise.  Key features of AIE include:

• Rigorous “Unit of Measure” definitions even for such normally subjective elements as customer satisfaction.
• Systematic uncertainty analysis - quantification of the risk of an IT investment in such a way that it can be directly compared with a non-IT investment.
• Calculation of the economic value of information - AIE works on the logic that information reduces uncertainty, less uncertainty improves decisions, better decisions result in more effective actions and effective actions improve profit or mission results.  AIE includes a mathematical framework for calculating an pricing all this in dollars!
• IT investments as an investment portfolio - this is perhaps obvious from the previous three features but the rigor of the previous three features allows the IT investments to be analyzed alongside, and compared using, the same tools as those used extensively for non-IT investments.

As often happens, i was looking for something else when I came across a 2008 article in IEEE Computer by Mary Lacity and Joseph Rottman (don’t worry - the subject matter is covered in their 2008 book, “Offshore Outsourcing of IT work).  Having just set one of our clients off on the implementation of some outsourcing options to supplement their in-house resources, the list of 20 major effects of offshore outsourcing reported by project managers caught my eye.  I strongly recommend that you think of this list as a set of risks that need to be mitigated in any outsourcing implementation.  With apologies to the authors, I have sorted their list according to my highest priorities.  The in-house Project Managers reported that they:

• needed a mentor the first time they managed a project with offshore resources
• had to motivate the supplier to share bad news
• had to make offshore suppliers feel welcome and comfortable
• needed to thoroughly verify the offshore supplier’s work estimates, which tended to be optimistic
• had to provide greater detail in requirements definitions
• had to do more knowledge transfer up front
• were forced to shortcut the knowledge transfer process because of deadlines set by senior IT leaders
• had to ensure that knowledge transfer was successful by testing the supplier employees’ knowledge
• had to set more frequent milestones
• needed more frequent and more detailed status reports
• required more frequent working meetings to prevent client-caused bottlenecks
• needed to accompany offshore suppliers to all client-facing meetings
• experienced higher transaction costs which threatened their ability to deliver projects on budget
• experienced project delays which threatened their ability to deliver projects on time
• had to guarantee that the supplier followed pre-agreed knowledge renewal practices
• had to ensure that the supplier transferred knowledge about new applications or technologies to the client
• had to learn about new applications or technologies independent of suppliers to ensure that the suppliers information and bids were valid
• had to integrate the suppliers CMM/CMMI processes into their own project management processes
• had to ensure that the supplier’s employees were fully trained as promised by the suppliers
• had to fill many of the roles the the PMO should have performed

I came across an interesting article in the May/June 2009 issue of IEEE Software by Magne Jurgensen of the Simula Research Laboratory in Norway which touches on the age-old challenge of deciding whether the lowest estimate is actually a realistic estimate.

From his research, Magne has derived 7 recommendations which I have paraphrased below (I suggest they have much validity for internal and external estimates):

1. If price is an important selection criteria, dont invite too many bidders if your requirements specification is incomplete.
2. Avoid using price as an important selection criteria if your ability to assess provider competence is low.
3. Ensure sufficient competence in assessing provider competence by hiring external experts if necessary.
4. When selecting bidders, compare the bids with your own independent cost estimate of the average of all bids.
5. Let providers collect sufficient information to help them analyze the projects complexity.
6. Don’t include potentially misleading information that could affect the estimation process (especially your budget expectations).
7. Be aware that a negotiation phase after receipt of bids may encourage even greater over-optimism.

Of course, “over-optimism” is Magne’s polite way of saying “stupidity.”  If these recommendations resonate with you, I suggest that you should talk to us here at DCG about getting a professional, objective view of your estimates and/or your estimation process.