Tag Archives: productivity

Not using UML on Projects is Fatal

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UMLThe Unified Modeling Language (UML) was adopted as a standard by the OMG in 1997, almost 20 years ago.  But despite its longevity, I’m continually surprised at few organizations actually use it.

Code is the ultimate model for software, but it is like the trees of a forest.  You can see a couple, but only few people can see the entire forest by just looking at the code.  For the rest of us, diagrams are the way to see the forest, and UML is the standard for diagrams.

They say, “A picture is worth a thousand words“, and this is true for code; even on a large monitor you can only see so many lines of code.  Every other engineering discipline has diagrams for complex systems, e.g. design diagrams for airplanes, blueprints for buildings.  In fact, the diagrams need to be created and approved  BEFORE the airplane or building is created.

Contrast that with software where UML diagrams are rarely produced, or if they are produced, they are produced as an after thought.  The irony is that the people pushing to build the architecture quickly say that there is no time to make diagrams, but they are the first people to complain when the architecture sucks.  UML is key to planning (see Not planning is for losers)

I think this happens because developers, like all people, are focused on what they can see and touch right now.  It is easier to try to code a GUI interaction or tackle database update problems than it is to work at an abstract level through the interactions that are taking place from GUI to database.

Yet this is where all the architecture is.  Good architecture makes all the difference in medium and large systems.  Architecture is the glue that holds the software components in place and defines communication through the structure.  If you don’t plan the layers and modules of the system then you will continually be making compromises later on.

In particular, medium to large projects (>10,000 function points) are at a very high risk of failure if you don’t consider the architectural issues.  Considering only 3 out of 10 software projects are successful only a fool would skip planning the architecture (see Failed? You get what you deserve!)

Good diagrams, in particular UML, allow you to abstract away all the low level details of an implementation and let you focus on planning the architecture.  This higher level planning leads to better architecture and therefore better extensibility and maintainability of software.

If you are a good coder then you will make a quantum leap in your ability to tackle large problems by being able to work through abstractions at a higher level.  How often do we find ourselves unable to implement simple features simply because the architecture doesn’t support it?

Well the architecture doesn’t support it because we spend very little time developing the blueprint for the architecture of the system.

UML diagrams need to be produced at two levels:

  • the analysis or ‘what’ level
  • the design or ‘how’ level

Analysis UML diagrams (class, sequence, collaboration) should be produced early in the project and support all the requirements.  Ideally you use a requirements methodology that allows you to trace easily from the requirements onto the diagrams.

Analysis diagrams do not have implementation classes on them, i.e. no vendor specific classes.  The goal is to identify how the high level concepts (user, warehouse, product, etc) relate to each other.

These analysis level UML diagrams will help you to identify gaps in the requirements before moving to design.  This way you can send your BAs and product managers back to collect missing requirements when you identify missing elements before you get too far down the road.

Once the analysis diagrams validate that the requirements are relatively complete and consistent, then you can create design diagrams with the implementation classes.  In general the analysis diagrams are one to many to the design diagrams.

Since you have validated the architecture at the analysis level, you can now do the design level without worrying about compromising the architectural integrity.  Once the design level is complete you can code without compromising the design level.

When well done the analysis UML, design UML, and code are all in sync.  Good software is properly planned and executed from the top down.  It is mentally tougher to create software this way, but the alternative is continuous patches and never ending bug-fix cycles.

So remember the following example from Covey’s The 7 Principles of Highly Effective People:

You enter a clearing where a man is furiously sawing at a large log, but he is not making any progress.  You notice that the saw is dull and is unable to cut the wood, so you say, “Hey, if you sharpen the saw then you will saw the log faster”.  To which the man replies, “I don’t have time, I’m too busy sawing the log”.

Don’t be the guy sawing with a dull

UML is the tool to sharpen the saw, it does take time to learn and apply, but you will save yourself much more time and be much more successful.

Bibliography

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Infeasible Projects: Executive Ignorance or IT Impotence?

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IDohDoh2nfeasible software projects are launched all the time and teams are continually caught up in them, but what is the real source of the problem?

There are 2 year actual projects for which the executives set a 6 month deadline.  The project is guaranteed to fail but isthis due to executive ignorance or IT impotence?

InfeasibleTimelineThere is no schedule risk in an infeasible project because the deadline will be missed.  Schedule risk only exists in the presence of uncertainty (see Schedule Risk is a Red Herring!!!)

As you might expect, all executives and IT manager share responsibility for infeasible projects that turn into death marches.  Learn about the nasty side effects Death March Calculus.

The primary causes for infeasible projects are:

  • Rejection of formal estimates
  • No estimation or improper estimation methods are used

Rejecting Formal Estimates

Infeasible

This situation occurs frequently; an example would be the Denver Baggage Handling System (see Case Study).

The project was automatically estimated (correctly) to take 2 years; however, executives declared that IT would only have 1 year to deliver.

Of course, they failed1.

The deadline was rejected by executives because it did not fit their desires.  They could not have enjoyed the subsequent software disaster and bad press.

When executives ignore formal estimates they get what they deserve.  Formal estimates are ignored because executives believe through sheer force of will that they can set deadlines.

If IT managed to get the organization to pay for formal tools for estimating then it is not their problem that the executives refuse to go along with it.

Improper Estimation Methods

The next situation that occurs frequently is using estimation processes that have low validity.  Estimation has been extensively studied and documented by Tom DeMarco, Capers Jones, Ed Yourdon, and others.

IceBergImproper estimation methods will underestimate a software project every time. Fast estimates will be based on what you can think of, unfortunately, software is not tangible and so what you are aware of is like the tip of an iceberg.

None of this prevents executives demanding fast estimates from development.  Even worse, development managers will cave in to ridiculous demands and actually give fast estimates.

Poor estimates are guaranteed to lead to infeasible projects (see Who needs Formal Measurement?)

Poor estimates are delivered by IT managers that:

  • Can’t convince executives to use formal tools
  • Give in to extreme pressure for fast estimates

Infeasible projects that result from poor estimates are a matter of IT impotence.

Conclusion

ChildWithIceCreamBoth executive ignorance and IT impotence lead to infeasible projects on a regular basis because of poor estimates and rejecting estimates; so there is no surprise here.

However, infeasible projects are a failure of executives and IT equally because we are all on the same team.  It is not possible for part of the organization to succeed if the other one fails.

IntestinalFortitudePossibly a greater share of problem is with IT management.  After all, whose responsibility is a bad decision — the guys that know what the issues are or the ones that don’t.

If a child wants ice cream before they eat dinner then whose fault is it if you cave in and give them the ice cream?

Unfortunately, even after 60 years of developing software projects, IT managers are either as ignorant as the executives or simply have no intestinal fortitude.

Even when IT managers convince executives of the importance of estimating tools, the estimates are routinely discarded because they do not meet executive expectations.

Rejection of automated estimates: productivity -16%, quality -22%

Until we can get a generation of IT managers that are prepared to educate executives on the necessity of proper estimation and be stubborn about holding to those estimates, we are likely to continue to have an estimated $3 trillion in failures of software projects every year.

End Notes

1For inquiring minds, good automated estimation systems have been shown to be within 5% of time and cost on a regular basis. Contact me for additional information.

References

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Don’t be a Slave to Your Tools

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Abstract SlaveDevelopers attach quickly to tools because they are concrete and have well defined behavior.  It is easier to learn a tool than to learn good practices or methodology.

Tools only assist in solving problems, they can’t solve the problem by themselves. A developer who understands the problem can use tools to increase productivity and quality.

Poor developers don’t invest the time or effort to understand how to code properly and avoid defects.  They spend their time learning how to use tools without understanding the purpose of the tool or how to use it effectively.

To some degree, this is partially the fault of the tool vendors.  The tool vendors perceive an opportunity to make $$$$$ based on providing support for a common problems, such as:

  • defect trackers to help you manage defect tracking
  • version control systems to manage source code changes
  • tools to support Agile development (Version One, JIRA)
  • debuggers to help you find defects

There are many tools out there, but let’s just go through this list and point out where developers and organizations get challenged.  Note, all statistics below are derived from over 15,000 projects over 40 years.1

Defect Trackers

Believe it or not, some organizations still don’t have defect tracking software. I’ve run into a couple of these companies and you would not believe why…

Inadequate defect tracking methods: productivity -15%, quality -21%

So we are pretty much all in agreement that we need to have defect tracking; we all know that the ability to manage more than a handful of defects is impossible without some kind of system.

Automated defect tracking tools: productivity +18%, quality +26%

The problem is that developers fight over which is the best defect tracking system. The real problem is that almost every defect tracking system is poorly set-up, leading to poor results. Virtually every defect tracking system when configured properly will yield tremendous benefits. The most common pitfalls are:

  • Introducing irrelevant attributes into the defect lifecycle status, i.e. creation of statuses like deferred, won’t fix, or functions as designed
  • Not being able to figure out if something is fixed or not
  • Not understanding who is responsible for addressing a defect

The tool vendors are happy to continue to provide new versions of defect trackers. However, using a defect tracker effectively has more to do with how the tool is used rather than which tool is selected.

One of the most fundamental issues that organizations wrestle with is what is a defect?  A defect only exists if the code does not behave according to specifications. But what if there are no specifications or the specifications are bad?  See It’s not a bug, it’s… for more information.

Smart organizations understand that the way in which the defect tracker is used will make the biggest difference.  Discover how to get more out of you defect tracking system in Bug Tracker Hell and How to Get Out.

Another common problem is that organizations try to manage enhancements and requirements in the defect tracking system.  After all whether it is a requirement or a defect it will lead to a code change, so why not put all the information into the defect tracker?  Learn why managing requirements and enhancements in the defect tracking system is foolish in Don’t manage enhancements in the bug tracker.

Version Control Systems

Like defect tracking systems most developers have learned that version control is a necessary hygiene procedure.  If you don’t have one then you are likely to catch a pretty serious disease (and at the least convenient time)

Inadequate change control: productivity -11%, quality -16%

Virtually all developers dislike version control systems and are quite vocal about what they can’t do with their version control system.  If you are the unfortunate person who made the final decision on which version control system is used just understand that their are hordes of developers out their cursing you behind your back.

Version control is simply chapter 1 of the story.  Understanding how to chunk code effectively, integrate with continuous build technology, and making sure that the defects in the defect tracker refers to the correct version are just as important as the choice of version control system.

Tools to support Agile

Sorry Version One and JIRA, the simple truth is that using an Agile tool does not make you agile, see this.

These tools are most effective when you actually understand Agile development. Enough said.

Debuggers

I have written extensively about why debuggers are not the best tools to track down defects.  So I’ll try a different approach here.

One of the most enduring sets of ratios in software engineering has been 1:10:100.  That is, if the cost of tracking down a defect pre-test (i.e. before QA) is 1, then it will cost 10x if the defect is found by QA, and 100x if the defect is discovered in deployment by your customers.

Most debuggers are invoked when the cost function is in the 10x or 100x part of the process.  As stated before, it is not that I do not believe in debuggers — I simply believe in using pre-test defect removal strategies because they cost less and lead to higher code quality.

Pre-test defect removal strategies include:

  • Planning code, i.e. PSP
  • Test driven development, TDD
  • Design by Contract (DbC)
  • Code inspections
  • Pair programming for complex sections of code

You can find more information about this in:

Seldom Used Tools

Tools that can make a big difference but many developers don’t use them:

Automated static analysis: productivity +21%, quality +31%

Automated unit testing: productivity +17%, quality +24%

Automated unit testing generally involves using test driven development (TDD) or data driven development together with continual build technology.

Automated sizing in function points: productivity +17%, quality +24%

Automated quality and risk prediction: productivity +16%, quality +23%

Automated test coverage analysis: productivity +15%, quality +21%

Automated deployment support: productivity +15%, quality +20%

Automated cyclomatic complexity computation: productivity +15%, quality +20%

Important Techniques with No Tools

There are a number of techniques available in software development that tool vendors have not found a way to monetize on. These techniques tend to be overlooked by most developers, even though they can make a huge difference in productivity and quality.

The Personal Software Process and Team Software Process were developed by Watts Humphrey, one of the pioneers of building quality software.

Personal software process: productivity +21%, quality +31%2

Team software process: productivity +21%, quality +31%3

The importance of inspections is covered in:

Code inspections: productivity +21%, quality +31%4

Requirement inspections: productivity +18%, quality +27%4

Formal test plans: productivity +17%, quality +24%

Function point analysis (IFPUG): productivity +16%, quality +22%

Conclusion

There is definitely a large set of developers that assume that using a tool makes them competent.

The reality is that learning a tool without learning the principles that underly the problem you are solving is like assuming you can beat Michael Jordan at basketball just because you have great running shoes.

Learning tools is not a substitute for learning how do do something competently. Competent developers are continually learning about techniques that lead to higher productivity and quality, whether or not that technique is supported by a tool.

References

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The Programmer Productivity Paradox

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Programmers seem to be fairly productive people.

You always see them typing at their desks; they chafe for meetings to finish so that they can go back to their desks and code. When asked, they will say that there is not enough time to produce the code, and the sooner they can start coding, the sooner they will be done.

So writing code must be the most important thing, correct?

If the average programmer writes about 50 lines of production code a day.  A 50,000 line program would take 1,000 man days to produce.  The 50,000 line listing can be entered by a programmer at about 1,000 lines a day or about 50 man days.

So what the heck are the developers doing for the other 950 days?

Before addressing that issue, lets make a simple observation. Capers Jones has compared many methodologies (RUP, XP, Agile, Waterfall, etc) and programming languages over thousands of projects and determined that programmers write between 325 and 750 lines of code (LOC) per month, which is less than the 1,000 LOC per month suggested above1.  Even if programmers do not average 50 lines of code per day, the following is clear2.

  • Methodology does not explain the apparent productivity gap
  • No language accounts for the apparent productivity gap

The reality is that only a fraction of a developer’s time is actually spent writing production code. If a developer is typing in code all the time then they are really trying different combinations of code until they finally find the combination of code that works.

Or more correctly, the combination that seems to match the requirements until either QA or the business analyst comes back and lets them know there is a problem.

That is why developers that plan their code before using the keyboard tend to outperform other developers. Not only do only a few developers really plan out their code before coding but also years of experience do not teach developers to learn to plan.  In fact studies over 40 years show that developer productivity does not change with years of experience. (see No Experience Required!)

Years of experience do not lead to higher productivity

Interestingly enough, there are methodologies that have been around for a long time that emphasize planning code.  Watts Humphrey is the created of the Personal Software Process (PSP)3.  Using PSP has been measured to:

PSP can raise productivity by 21.2% and quality by 31.2%

If you are interested there are many other proven methods of raising code quality that are not commonly used (see Not Planning is for Losers).

If your developers at their keyboard and not planning at a white board then odds are that your productivity is not as high as it could be.

Bibliography

1 The The Mythical Man Month is even more pessimistic suggesting that programmers produce 10 production lines of code per day
2 Jones, Capers and Bonsignour, Olivier.  The Economics of Software Quality.  Addison Wesley.  2011
3 Watts, Humphrey.  Introduction to the Personal Software Process, Addison Wesley Longman. 1997

Other articles in the “Loser” series

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Make no mistake, I am the biggest “Loser” of them all.  I believe that I have made every mistake in the book at least once 🙂

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Debuggers are Crutches

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Woman on CrutchesDefects are common, but they are not not necessary.  They find their way into code because:

Defects are only corrected by understanding pathways and debuggers are not the best way to do this.

Debuggers are commonly used by developer’s to understand a problem, but just because they are common does not make them the best way to find defects.  I’m not advocating a return to “the good old days” but there was a time when we did not have debuggers and we managed to debug programs.

Avoid Defects

Personal Software ProcessThe absolute best way to remove defects is simply not to create them in the first place. You can be skeptical, but things like  the Personal Software Process (PSP) have been used practically to prevent 1 of every 2 defects from getting into your code.  Over thousands of projects:

The Personal Software Process increases productivity by 21% and increases code quality by 31%

A study conducted by NIST in 2002 reports that software bugs cost the U.S. economy $59.5 billion annually. This huge waste could be cut in half if all developers focused on not creating defects in the first place.

Not only does the PSP focus on code planning, it also makes developers aware of how many defects they actually create.  Here are two graphs that show the same group of developers and their defect injection rates before and after PSP training.

Before PSP training After PSP training

Finding Defects

Unskilled professionalUsing a debugger to understand the source of a defect is definitely one way.  But if it is the best way then why do poor developers spend 25 times more time in the debugger than a a good developer? (see No Experience Required!)

That means that poor developers spend a week in the debugger for every 2 hours that good developer does.

No one is saying that debuggers do not have their uses.  However, a debugger is a tool and is only as good as the person using it.  Focus on tools obscures lack of skill (see Agile Tools do NOT make you Agile)

If you are only using a debugger to understand defects then you will be able to remove a maximum of about 85% of all defects, i.e. 1 in 7 defects will always be present in your code.

Orkin manWould it surprise you to learn that their are organizations that achieve 97% defect removal?  Software inspections take the approach of looking for all defects in code and getting rid of them.

Learn more about software inspections and why they work here:

Software inspections increase productivity by 21% and increases code quality by 31%

Even better, people trained in software inspections tend to inject fewer defects into code. When you become adept at parsing code for defects then you become much more aware of how defects get into code in the first place.

But interestingly enough, not only will developers inject fewer defects into code and achieve defect removal rates of up to 97%, in addition:

Every hour spent in code inspections reduces formal QA by 4 hours

Conclusion

As stated above, there are times where a skilled professional will use a debugger correctly.  However, if you are truly interested in being a software professional then:

  • You will learn how to plan and think through code before using the keyboard
  • You will learn and execute software inspections
  • You will learn techniques like PSP which lead to you injecting fewer defects into the code

You are using a debugger as a crutch if it is your primary tool to reduce and remove defects.

Related Articles

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Make no mistake, I am the biggest “Loser” of them all.  I believe that I have made every mistake in the book at least once 🙂

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The Programmer Productivity Paradox

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Programmers seem to be fairly productive people.  You always see them typing at their desks; they chafe for meetings to finish so that they can go back to their desks and code. When asked, they will say that there is not enough time to produce the code, and the sooner they can start coding, the sooner they will be done.

So writing code must be the most important thing, correct?

If the average programmer writes about 50 lines of production code a day.  A 50,000 line program would take 1,000 man days to produce.  The 50,000 line listing can be entered by a programmer at about 1,000 lines a day or about 50 man days.

So what the heck are the developers doing for the other 950 days?

Before addressing that issue, lets make a simple observation. Capers Jones has compared many methodologies (RUP, XP, Agile, Waterfall, etc) and programming languages over thousands of projects and determined that programmers write between 325 and 750 lines of code (LOC) per month, which is less than the 1,000 LOC per month suggested above1.  Even if programmers do not average 50 lines of code per day, the following is clear2.

  • Methodology does not explain the apparent productivity gap
  • No language accounts for the apparent productivity gap

The reality is that only a fraction of a developer’s time is actually spent writing production code. If a developer is typing in code all the time then they are really trying different combinations of code until they finally find the combination of code that works.  Or more correctly, the combination that seems to match the requirements until either QA or the business analyst comes back and lets them know there is a problem.

That is why developers that plan their code before using the keyboard tend to outperform other developers.  Not only do only a few developers really plan out their code before coding but also years of experience do not teach developers to learn to plan.  In fact studies over 40 years show that developer productivity does not change with years of experience. (see No Experience Required!)

Years of experience do not lead to higher productivity

Interestingly enough, there are methodologies that have been around for a long time that emphasize planning code.  Watts Humphrey is the created of the Personal Software Process (PSP)3.  Using PSP has been measured to:

PSP can raise productivity by 21.2% and quality by 31.2%

If you are interested there are many other proven methods of raising code quality that are not commonly used (see Not Planning is for Losers).

If your developers at their keyboard and not planning at a white board then odds are that your productivity is not as high as it could be.

Bibliography

1 The The Mythical Man Month is even more pessimistic suggesting that programmers produce 10 production lines of code per day
2 Jones, Capers and Bonsignour, Olivier.  The Economics of Software Quality.  Addison Wesley.  2011
3 Watts, Humphrey.  Introduction to the Personal Software Process, Addison Wesley Longman. 1997

Other articles in the “Loser” series

Want to see more sacred cows get tipped? Check out:
Moo?

Make no mistake, I am the biggest “Loser” of them all.  I believe that I have made every mistake in the book at least once 🙂

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Who should set defect priority?

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ControlFreakSurprisingly, defect priority should not be set by QA.  QA are generally the owners of the defect tracking system and control it, but this is one attribute that they should not control.  The defect tracker is a shared resource between QA, engineering, engineering management, and product mangement and is a coordinating mechanism for all these parties.

Commonly people mix up priority and severity, for example, there may be a severe defect that causes the software either not to install or to cease functioning.

Car won't startIt is common for new releases to have various installation problems when it initially gets to QA. This blocks QA so they mark the defects with a high severity and high priority.  This issue has a high severity and needs to be addressed right away, but remember bug tracking systems are append only — once this defect gets into the system, it will never get out.  This kind of issue should be escalated to the engineers and engineering management because it makes little sense to clog the defect tracking system with it.

Now there may be intermittent issues that cause the software to fail and you may assume that this defect would be high priority, but if this defect occurs very rarely and would cost too much to fix then this defect may be a low priority.  Once again the priority of an intermittent severe issue can not be determined by QA.

Similarly, there may be many cosmetic or minor defects where fixing them might make a huge difference in the user experience and reduce support calls.  Even though these defects are minor, they may be easy to fix and save you serious money.  Once again, this can not be decided by QA.

Therefore, QA should reserve the right to set the initial severity, and may have an internal field for QA priority, but the priority of a defect should be determined by a product manager (PM) who has a more complete understanding of the overall context of the product.  The priority of a defect is a business issue, not an engineering or QA issue.

Ideally, the product manger will set the priority of a defect during a defect triage session where representatives from engineering, engineering management, and QA are present. As you go through each new defect each person can present their logic for what the priority should be.  The PM should then set the priority defect and assign the release it should be fixed in (i.e. this release, minor release, major release, won’t fix).  Ideally there are extra fields in the defect record for both QA and development to put their priority beliefs.

It is important that the status of a defect not include any of the following or your defect tracker will go to hell (see Bug Tracker Hell and How to Get Out):

  • Priority
  • Severity
  • Fix version

Big documentIt is very hard to generate meaningful reports when these attributes creep into the defect status.  You know that this has happened if you have a multi-page training manual for entering defects into the system.  Some of the statuses that make sense initially but turn the defect system into a nightmare are:

  • FAD (functions as designed)
  • WontFix
  • NextVersion
  • CantReproduce

Of course, if you don’t have regular bug triage sessions with all the parties mentioned above then you are probably sand-bagged in fire-fighting.

Other articles:

 

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Enhancements don’t belong in the bug tracker

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Not my faultAs development progresses we inevitably run into functionality gaps that are either deemed as enhancements.

These issues often get captured by QA in the bug tracker and assigned to a developer.

Enhancements should not be managed from the bug tracker

The life cycle of a defect and the life-cycle of a enhancement are two entirely different things.  A defect is a difference between a stated requirement and the code. If there is no documentation there is no code defect (see It’s not a bug, it’s…) — in fact, most enhancements will eventually be coded by some developer; they just should not be managed from the bug tracker.

Defect Life-cycleDefectLifecycle

The defect life-cycle is well known:

  • Defect is identified as a departure from the requirements
  • Defect is assigned to a developer
  • Defect is corrected
  • Correct is verified
    • If not corrected re-open and re-assign to developer
  • The defect is closed

This is the incorrect way to manage enhancements.  When a functionality gap is determined by QA and it is not covered by the requirements then we have an issue.  It is rarely the case that the issue can be resolved by the developer.

Enhancement Life-cycle

If enhancements are assigned to a developer then they are likely to try to resolve the issue. The problem is that “enhancements” determined at the QA level may be phantom problems caused by either:

  1. Insufficient requirements
  2. Correct requirements but incorrect test plans

Enhancements may or may not become code changes.  Even when enhancements turn into code change requests they will generally not be implemented as the developer or QA think they should be implemented.

Enhancements are really requirement defects. Enhancements should be logged as such in the bug tracker and assigned to the person in charge of requirements (business analyst or product manager).  Those individuals should be responsible to track down how these issues should be handled.

If the requirements are correct and the test plans are defective then it should be logged as a test defect.  This is tricky because QA often controls the bug tracker and will not log errors that they have made.

At a minimum, the implementation of requirements and test defects can do several positive things for you:

  1. It removes the responsibility to find a solution from development.
  2. It makes it clear how many defects are in the requirements or test plans.
  3. It reduces stress; no developer wants to be blamed for an issue that is not his.
  4. Many enhancements call for updated project plans and pushing back the deadline.

Put Responsibility Where it Belongs

The creation of requirements and test defects in the bug tracker goes a long way to cleaning up the bug tracker.  In fact, requirements and test defects represent about 25% of defects in most systems (see Bug Tracker Hell and How to Get Out!).  The percentages break down as follows:

  • Requirements defects: 9.58%
  • Testing defects: 15.42%

The creation of requirement and test defects in the bug-tracker alleviate pressure on the engineering department and redirect it  to either the product manager or QA. Eventually enough data will accumulate in the bug-tracker to get management’s attention.

At a minimum, these categories should help reduce the amount of fire-fighting in late projects (Root cause of ‘Fire-Fighting’ in Software Projects)

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It’s not a bug, it’s…

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When does a bug become a bug?
Who decides that it is a bug?

Caution, BugHow many legs does a lamb have if I say the tail is a leg?  The answer is 4, just because I say the tail is a leg does not make it a leg!

Bugs should be obvious, but we say It’s not a bug, it’s a feature because often it isn’t obvious.  Watson Humphrey felt that we should use the term defect and not bug because most people don’t take bugs seriously, so let’s use the term defect instead.

So when does a defect become a defect?

  • When quality assurance tells you that you have a defect?
  • When product management says that it is a defect?
  • When the customer says that it is a defect?

The answer is: none of the above.

Now it might turn out that there is a problem and that code needs to change, but a defect only exists if:

code behaves differently than the requirements specification

This is important because most systems are under specified (if they are specified at all) and so when code misbehaves it is only a defect if the code behavior differs from the specification.  We call defects undocumented features because we know that the problem is that the requirements were never written.

Incomplete and Inconsistent Requirements

Many organizations do not create sufficiently complete requirements before starting development, either because they don’t know how to capture requirements properly or because they don’t have resources capable of capturing complete requirements. Incomplete (and inconsistent) requirements and unrealistic deadlines often force developers into making decisions about how to implement features.  The end result is that developers are regularly told that they have defects in their code.

While this process is common, it is destructive.  When requirements are under specified and inconsistent developers end up needing to perform serious rework. The rework will can require dramatic changes that will impact the architecture of the code.

The time required to find a work around (if it is possible) is rarely included in the project plan. Complicating matters is that the organizations that are reluctant to spend time creating requirements also tend to underestimate their projects.  This puts tremendous pressure on the engineering department to deliver; this promotes the 5 worst practices in software development (see Stop It! No… really stop it.)

Only 54% of Issues are Resolved by Engineers

The attitude that all defects must be resolved by the engineering department is severely misguided.  Analysis by Capers Jones of over 18,000+ projects shows that only about 54% of all defects can be resolved by the engineers! (only the 3 highlighted rows below)

Defect Role Category Frequency Role
Requirements defect 9.58% BA/Product Management
Architecture or design defect 14.58% Architect
Code defect 16.67% Developer
Testing defect 15.42% Quality Assurance
Documentation defect 6.25% Technical Writer
Database defect 22.92% Data base administrator
Website defect 14.58% Operations/Webmaster

This means that precious time will be wasted assigning issues to developers that they can not resolve.  The time necessary to redirect the issue to the correct person is a major contributing factor to fire-fighting

Getting Control of the  Defect Process

For most organizations fixing the defect process involves understanding and categorizing defects correctly.  Organizations that are not tracking the different sources of defects probably have a bug tracker that has gone to hell.  Here is how you can fix that problem, see Bug Tracker Hell and How To Get Out!

At a minimum you need to implement the requirements defect, once you identify issues that are caused by poor requirements it will shine the white hot light of shame onto the resources that are capturing your requirements.  Once you realize how many requirements defects exist in your system you can begin to inform senior management about the requirements problem.

Reducing Fire-Fighting

Fire fightingThe best way to reduce fire fighting is to start writing better requirements (or writing requirements 🙂 ).  To do so you need to figure out which of the following are broken:

  1. Not enough time is allocated to the requirements phase
  2. Unskilled people are capturing your requirements

In all likelihood both of these issues need to be fixed in your organization.  When requirements are incomplete and inconsistent you will have endless fire-fighting meetings involving everyone (see Root cause of ‘Fire-Fighting’ in Software Projects)

Stand your ground if someone tells you that you have coded a defect when there is no documentation for the requirement.

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Let the Machines Take Over

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Whether you believe that the machines will take over or not will always be up for debate (until they actually take over :-)).

What is not up for debate is that many processes for software creation have been automated and can help improve productivity and quality in every phase of a software project.

Here are some statistics about how useful automation can be and give you some ammunition when you confront management about the need for these tools.

These results have been generated by Capers Jones who has been collecting data for over 15,000 projects for about 40 years.  Full results shown in the paper in the references.

Requirements Analysis

There have been tools available for tracing requirements for quite some time. Generally the only companies forced to provide requirements traceability, but this makes sense to do on all large projects:

Automated requirements tracing can raise productivity by 12.89% and quality by 17.8%

There are tools that can help size programs in terms of function points, which then allows you to get relatively accurate estimates of a project`s cost and time before the project is executed.

Automated sizing in function points can raise productivity by 16.5% and quality by 23.7%

Development

There are several tools that are useful during development, but by far the biggest bang for your buck comes in the form of automated static analysis.

Automated static analysis can raise productivity by 20.9% and quality by 30.9%

There are now static analysis tools available for virtually all current languages that are cost effective and can help to locate those hard to find occasional defects.  You can find a list here of automated tools for C/C++, Java, JavaScript, Objective-C, Perl, PHP, and Python.

Then getting cyclomatic complexity counts for your code can be done in an automated fashion.  Studies have shown that files with a count of 74+ were 98% likely to have defects.

Automated cyclomatic complexity analysis can raise productivity by 14.5% and quality by 19.5%

Performance is a tricky thing, we will talk about it often but rarely do we actually do something about it. Automated performance analysis allows you to determine if there will be production problems before deploying new code.

Automated performance analysis can raise productivity by 12.5% and quality by 19.5%

There are tools for restructuring code automatically, many of these are built directly into today’s IDEs.  There are many stand alone tools for automated restructuring, some are shown here.

Automated restructuring can raise productivity by 8.0% and quality by 11.7%

Automated unit testing is the easiest way to detect defects early.  If your developers are using Test Driven Development (TDD) and any continuous integration tool  (i.e, Hudson or Jenkins) then you can find out with each build if a defect has crept into the code.

Automated unit testing can raise productivity by 16.5% and quality by 23.8%

Testing

Believe it or not I was working with a team that was not using version control or defect tracking last year. So there are clearly still some organizations not using defect tracking.

Automated defect tracking can raise productivity by 17.5% and quality by 25.9%

Testing the percentage of the application pathways that are actually being tested is critical.  Most of your defects will lie in the pathways that you can not test.

Automated test coverage analysis can raise productivity by 15.5% and quality by 21.2%

Test cases can be generated automatically in some situations.

Automated test case generation can raise productivity by 15.0% and quality by 20.0%

Deployment

The next two tools are not about technology that you acquire, rather it is about building automated tools to support critical deployment functions.  When the pressure is on it is tough to think about creating applications to manage configuration in an automated way.  When management tells you that it would be a luxury to build these tools then show them these results.

Some organizations rely on manually updating configurations, but can be error prone if there are many values to update.

Automated configuration control can raise productivity by 16.4% and quality by 23.5%

Also, when automated configuration tools don`t exist then you often don`t have automated deployment tools.

Automated deployment support can raise productivity by 14.6% and quality by 19.6%

Conclusion

One of the most cost effective ways to improve productivity and quality in your software development is to automate any of the issues above.  In most cases you can find a vendor that will provide you support; for deployment you can make the case that building automated tools is the way to go.

References

1N.B. All productivity and quality percentages were derived over 15,000+ actual projects

Articles in the “Loser” series

Want to see sacred cows get tipped? Check out:
Moo?

Make no mistake, I am the biggest “Loser” of them all.  I believe that I have made every mistake in the book at least once 🙂

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