A Software Tester’s journey from manual to political tester

I wrote this some years ago. I should simplify the context and incorporate what I reference from the OP and other responders, so that it stands alone. But this has  meaningful observations which took effort to reach, so I’m putting a copy up here to start with.

Discussion on Testing Geek  25 comments    4 years ago

Wow, no exaggeration! I can see every event that befell poor Jim happening in the real world. HOWEVER, Jim’s a fortunate fellow, he has management attention at all! AND they look at results. AND there is a perception (no matter how shakily based) of overall product quality.

Jim was no worse than anyone else until he got automation started and mistook his personal satisfaction and enjoyment for the company’s obvious goal of shipping a stable or improving level of quality with fast turnaround on bugs and needed enhancements. This is engineering, not art. Its not self actualization, its a commercial business or a service enterprise which creates value.

All the way down this sad story, Jim accepts product failures, and testing failures. You Can Never Ignore Failures. Period. He should have turned political at that point and realized that Test, like anything, needs to be sold, shown to be valuable and productive, and needs allies. Therefore, tests need to actually be valuable and productive, and needs to make it easy for people to accept them, adopt them, and feel they are important support in their own success. Therefore he needed to measure success, as understood by his customers (developers, support, users) and maintain or improve its integrated value. Accepting failures leads to dead astronauts, wasted billions, wrongful convictions, Senate Select Committees, Frontline specials, sub-Redits, and worse.

Instead of seeing failures as a very, very, high priority, Jim turns into a man with a solution, wandering around, looking for a way to apply it. A tawdry tale, rendered no less tawdry by its oft retelling. Not insignificantly, Jim’s manager is clearly a weak and ineffective character who should have seen problems coming, or reacted when they appeared. Once Jim had made the case for automation, they might have hired someone who knew something about automation, or contracted with very carefully defined goals.

Jim might have split his team up front. He needed manual testers, who carried on the work that had been being done, with as much success as possible, and brain power applied to improve results and lower cost. A front line to hold success. Then a test automation group who focused on test automation with clear and obvious benefits

The automation environment needed to be something:

• …anyone could run;
• … which worked from a shippable product, as well as a release candidate or development build;
• …which could be triggered directly from a product build, so the build group-and-release group ran it every time;
• …which could be configured to run anything from a single, new, test to all existing tests
  • in a developer’s environment, before check-in, or
  • at any subsequent point, including on a previously shipped release with a support issue.

Setting up the test environment, creating a test to get the product to say “Hello world”, and recognizing that as a test pass ought to take no more than an hour longer than simply setting up the product. That assumption has to be proved every release or two with a calibrated innocent new-hire from somewhere.

Since all tests start by installing the product, license, etc, and starting it, the first thing to automate would be that. If there were changes in that functionality, over product history, the automation could start with the newest, but it had to support them all. Having this ‘smoke test’ be part of a full build would pay dividends to everyone, everywhere, and by designing with backward compatibility and future adaptability, thrash could be minimized, or at least recognized.

This would be a good time to look through the bugbase to determine where the most bugs were being found, where the most escapes were happening, and where the most critical bugs were coming from. Besides looking backward, a forward look at the product roadmap and with developers and management could highlight areas of future leverage

In parallel with automation, all of the above should be considered when selecting manual tests. Tests which never fail should be reduced relative to tests which find failures. Something that fails for diverse reasons may be a great canary in the coal mine, or might be a too fragile sponge that soaks up maintenance effort. In any event, continual improvement of the manual testing should run in parallel with introducing automation. After a small number of releases, the manual tests available should exceed the resources to run them. Selection of the ‘vital few’ should in intentional, not happenstance.

Most people can see the limitation of record and play back, so things should never stop there. The only tools worth looking at are tools that can be edited and rapidly expanded by iteration. Cut and paste is the lowest form of iteration and rapidly grows nightmares. Algorithmic test point generation is desirable, but data driven testing should get strong consideration. Algorithmic generation of literal tables which are then applied as tests separates the thing that needs to be done over and over, running the test, from the thing which is done less frequently, generating the test points.

In my life, I’ve seen a few of the failures in Jim’s story, but a lot of failures of usability by others, or by anyone, complete lack of testing in development plans. Test suites (with running tests) abandoned and no-longer run, until the next great hope get started. And far too little curiosity about which tests should be run, automatically or manually, to get the most bang for the buck.

Like I said, Jim is lucky!

View in discussion

Lets re-learn Python!

OK: here we go. I learned enough Python to write some, and to follow a lot of Jesse & Co’s at VMware. But I didn’t write all that much, I couldn’t check in anything, because there was not way to  test check-in candidates BEFORE going live. Or, at least, I couldn’t find one. And when I asked for help, I didn’t get what I needed.

But now I’m re-learning, since everyone says they want want proficiency in Python in their new hires. Better brush up on it then. .

So step one.  The canonical program in any Python book goes something like:

print (‘Lesson_1.py with single quote’)
print (2 ** 902)

to show off the easy familiarity Python has with very large numbers.

So I expanded on that. More print statements and if else and elif, Adding a demo of indents being isolated – The block for “if” must be all the same indent, the block for  else need to all be the same. But nothing requires the “if” block to match the “else” block. All they have to be is the same within themselves. Parseable.

Next, since we’re always printing things, what does “print()” return? Not-1, according to the if-then. If we print it, its “None”.  And we can test that it equals “None” (string equals is “==”. It does equal “None”.

But not only does it NOT not equal “none”, you can’t ask that question, without declaring/creating a “none”.  But its not a compile time call. The power of late binding is that nobody has checked “none” (or “NoNe”) until the “==” gets it.

And we get a lovely error:

“what comes back when we print one char
None
no char indent
None = print returned 1 or thereabouts
Traceback (most recent call last):
File “lesson_1.txt”, line 40, in <modul
if none == print(” no char indent”):
NameError: name ‘none’ is not defined”

And now our canonical program has an error, so we can canonically use the “try”, “except”, “finally”  statents.

And if we’re  really lucky, the response will have an error and we’ll get a SECOND TRIP through the error handler!

C:\Users\wabbott\python\Lesson_1>python lesson_1.txt
Lesson_1.py with double quote
Lesson_1.py with single quote
3381084999268257576654974623465706281720622886631177741618948537770712976363039
one char indent
else four char indent
elif six char indent
no char indent
print returned not-1 or thereabouts
what comes back when we print one char indent
None
None == print()
None == print returned 1 or thereabouts
we always do this, but don’t make any mistakes!
Traceback (most recent call last):
File “lesson_1.txt”, line 57, in <module>
if none == print(“none == print()”):
NameError: name ‘none’ is not defined

and there we go.

 

# Lesson_1.py
# picking-up the Python thread again, 5 years later.
# All the recruiters hope I know it, better look into that and perhaps I can find a job.
#

#!/usr/bin/python – ha!

try:

print (“Lesson_1.py with double quote”)
print (‘Lesson_1.py with single quote’)
print (2 ** 902)

# Python uses indentation instead of curly braces to identify blocks. Kind of a nice idea.

if 1:
print( ” one char indent”) # this one prints
else:
print( ” two char indent”)
if 0:
print( ” if three char indent”)
else:
print( ” else four char indent”) # this one prints

 

if 0:
print( ” if five char indent”)
elif 1:
print( ” elif six char indent”) # This one prints
elif 0:
print( ” elif seven char indent”)
elif 1:
print( ” elif eight char indent”)

 

 

if print(“no char indent”):
print(” print returned 1 or thereabouts”)
else:
print(”  print returned not-1 or thereabouts”)

print ( print (” what comes back when we print one char indent”))

 

if (None == print(“None == print()”)):
print(” None == print returned 1 or thereabouts”)
else:
print(” None != print returned not-1 or thereabouts”)

 

if none == print(“none == print()”):
print(” none == print returned 1 or thereabouts”)
else:
print(” none != print returned not-1 or thereabouts”)

 

if NoNe == print(“NoNe == print()”):
print(” NoNe ==44 print returned 1 or thereabouts”)
else:
print(” NoNe != print returned not-1 or thereabouts”)

#  except Argument:
# print(“The argument is>”, Argument, “< ” )

print(“And look, now it fell through!”)

finally:

print(“we always do this, but don’t make any mistakes!”)

# ———-X———-X———-X———-X———-X———-X———-X———-X———-X———-X

Software Test Methods, Levels, quiz question answers

Quiz questions about software test. My answers are probably longer than was hoped for, but specific, and most important, true and demonstrable.

1) What is the difference between functional testing and system testing?

2) What are the different testing methodologies?

1) System test is the equivalent of actual customers/users using the product. Carried out as if in the real world, with a range of detailed configurations, simulation of typical users working in typical way. It is one level of abstraction above Functional testing. Functional Test verifies that the product will do functions which it is intended to do. Play, rewind, stop, pause, fast forward. +, -, x, /, =.  Functional Tests must be drawn from the Requirements documents. System Test checks that a product which meets those requirements can be operated in the real world to solve real problems. Put another way, System test proves that the requirements selected for the product are correct.

This makes one wonder why engineers don’t do system test on the requirements before creating the design and code… mostly because its hard to do, and they’re sure they understand what the requirements should be, I suppose. I’ve never seen it done in depth.

 

2) “the different testing methodologies” seems over-determined. The following are ‘some’ different testing methods. There may be others.

Perhaps the intent of the question is to expose a world divided into White Box and Black Box testing, which are different from each other. But there are other dichotomies, in addition to White Box and Black Box.

Software testing methods divide into two large classes, Static and Dynamic. Static testing looks at source code, dynamic testing requires executable programs and runs them. Another division is between Using a Tool that evaluates source code and and Checking Program Output. Within either set of large groups are smaller divisions, Black Box and White Box (and Clear Box and Gray Box) are all divisions of Dynamic or Checking Output methods.  Specific methods within the large groups include

• running source code through a compiler
• running a stress test that consumes all of a given resource on the host
• running a tool that looks for memory allocation and access errors
• doing a clean install on a customer-like system and then running customer-like activities and checking their output for correctness.

Orthagonal to all of the above, Manual Test and Automated Test are infastructure-based distinctions, Automated tests may be Black Box, Unit, running a tool, checking output, or any other methodology. Manual and Automated are meta-methods.

 

Static Software Test Methods: Similar to, but not exactly the same as Tool Using Methods, to find problems in software source code.

2.1) Compile successfully, no errors or warnings. This is the first step before inspection, since nothing is better or cheaper at finding compiler problems than the compiler.

2.2) Inspection and code review, to see if the code is written to the standards that the organization enforces. I like and use code reviews, the formal Fagan system, and less formal “extreme programming” techniques like having a second person review all diffs or do a walk through with two people at the workstation. They work. The standards inspected for are usually helpful in preventing bugs or making them visible. Just looking usually improves product quality – the Western Electric effect if nothing else.

There may be some insight into product requirements and how the code meets them in a review. But the reviewers would need to know the requirements and the design of the software in some detail. Its difficult enough to get the code itself to be read. In Engineering Paradise, I suppose the requirements are formally linked to design features, and features to data and code that operates on that data, to create the feature.

2.3) Static analysis. Besides passing compiler checks without errors or warnings, there are static analysis tools, “lint” for example, that can inspect code for consistency with best practices and deterministic operation. Coverity, and others, have commercial products that do static test on source code.

2.4) Linking, loading. The final static events are linking the code and libraries required to complete the application, and writing a usable file for the executable, which the loader will load.

Dynamic Software Test Methods:

2.5) Memory access / leakage software test. Rational/IBM’s Purify, like ValGrind and BoundsChecker, run an instrumented copy of the source code under test to see memory problems in a dynamic environment. Its run and the results should be checked and responded to before a large investment in further  Dynamic testing should happen.

2.6) Performance test. Measuring resources consumed, obviously time, possibly others, during repeatable, usually large-scale, operations, similar to System or Load tests. Generic data, from development testing, is necessary and may be shipped as an installation test to users. Proprietary data, under a NDA (non-disclosure agreement), may also be needed, for complex problems ans/or important customers. In normal operation, the actual outputs are not looked at, at most, spot-checked, and the tool(s) keeping track of resources are the basis of pass/fail.

2.7) Installation Test. Typically a subset of in-house performance tests, with optional, generic, data. The performance recorded is comparable between releases, instances, configurations, sites, customers, and the software maker’s own in-house performance tests. Customers can use Installation tests to verify their hardware/software environment, benchmark it, evaluate new purchases for their environment, etc.

 

Checking Program Output Methods:

After tool based dynamic testing, the rest of Dynamic software test is based on running the product with specific inputs and checking the outputs, in detail.

Checking can be done with with exit status, stack traces,”assert()”, exceptions, diffing large output files against ‘gold’ references, log searches, directory listings, searching for keywords in output streams indicating failure or incorrect operation, checking for expected output and no other, etc. No test failures are acceptable. Each test must be deterministic, sequence independant, and (ideally) can run automatically. No judgement required for results. All require running the program.

2.8) Unit tests of pieces of the a product, in isolation, with fake/simulated/mock resources. A great bottom-up tool for verifying software. At the unit test level is where knowledge of the code is most important to testing. It is white box/clear box, with full insight into the code under test. One explicit goal of unit test should be forcing all branches in the code to be executed. That can’t be done without allowing visibility into the code.

2.9) Integration Test. The next level above unit test, the tests of code which calls code which calls code… and the code above that! The point is that integration is where code from different groups, different companies, different points in time, certainly different engineers, comes together. Misunderstanding is always possible. Here’s one place it shows up. Visibility into the code is getting dimmer here. Some tests are more functional, if a subsystem contains complete, requirement-satisfying functions.

2.10) Functional Test. Verifying that the product will do functions which it is intended to do. Play, rewind, stop, pause, fast forward. +, -, x, /, =.  Tests here should be drawn from the Requirements documents. Things that should be tested here should start in the Requirements docs. Each requirement has to be demonstrated to have been met. Its black-box testing, run from the interface customers use, on a representative host, with no insight into the internals of the product. Unless the requirements specify low level actions.

Its not particularly combinatorial- a short program, a long program, 2+2, 1/-37. Pat head. Rub belly. Walk, Not all 3 at once.

If a word-processor has no stated limit for document size, you need to load or make a really big file, but, truly, that’s a bad spec. A practical limit of ‘n’ characters has to be agreed as the maximum size tested-to. Then you stop.

All these Tests should be drawn from the Requirements documents. Things that should be tested here should start in the Requirements docs.

All that Verification is good, but what about Validation?

Unit test,  Integration test, or Functional Test, is where Validation, proving correctness of the design, might happen. Validation test is where deep algorithms are fully exercised, broad ranges of input are fully exercised, Tests that include all possible numerals, all possible characters, all defined whitespace, read in or written out. Numbers from MinInt to MaxInt, 0 to MaxUnsigned, the full range of Unicode characters, etc., etc., are exercised.

(Errors in input numbers should be seen in System test anyway, but accepting a wide range goes here.) This is not always done very formally, because most modern code environments don’t need it. But someone ought to look at least once.

L10n (Localization) and I18n (Internationalization) that need to be selected at link time or run time can be checked here too.
This is also where path-length limits, IPv-6 addresses, etc. should be checked.

2.11) User interface test verifies the controls and indicators that users at various levels see, hear, touch, operate and respond to. This is separate from any actual work the program may do in response. This is a high-value target for automation, since it can be complex and tedious to do UI testing in great detail by hand.

2.12) System Test. Full up use of the system. Training, white-paper and demo/marketing examples. Real-world situations reproduced from bugs or solutions provided for customers. Unless requirements included complexity, this is where the complex tests start. Huge data. Complex operations.  The range of supported host configurations, min to max, gets tested here too.

We’ll want to see all the error messages, created every possible way. We’ll want to have canned setups on file, just like a customer would, and we pour them into the product, run it, and collect the output. The set pass/fail on the output.

Somewhere between System Test and Acceptance test, the scale of pass/fail goes up another level of abstraction. Software test pass/fail results are one in the same with the product pass / fail. If data and setup are good, it should run and pass. Ship the result. If the data and/or setup have a problem, it should run and fail. The failure should propagate out to be stored in detail, but in the end this is a trinary result. Pass, Fail, Not Proven

2.13) Load test, Stress test.  Load tests go to the point that all of a resource is consumed, and adding  more activity produces no more output in real time. Resources include CPU, memory, local storage, networked storage, video memory, USB ports, maximum number of users, maximum number of jobs, maximum instances of product, etc. Stress test adds data, jobs, etc, clearly (110% or more) above load test maximum.

2.14) Stability test. Long term test. Stability test and long-term test are where a server or set of servers are started and left running, doing real work, for days, weeks, months. Some of the tests must repeat inputs and expect identical outputs each time.  Resource consumption should be checked. Its fair for the application or tool to have the node to itself, but adding other applications and unrelated users here and in the Load/Stress tests is meaningful, to avoid surprises from the field.

2.15) Acceptance test.  Customer sets-up their run-time world use of the system and uses it. Everything they would normally do. If its a repeat sale, they may just clone the previous installation. Run the previous and the new system, release, patch, etc, and compare output to installed software on machines that customer likes and trusts. If the product is a new one, acceptance means judging pass-fail from the output produced.

 

Many other kinds of test are mentioned in conversation and literature. A web search will turn up dozens. Regression test, stability test, in the sense that a new code branch is stable, sanity test and smoke test are all forms of testing but usually, in my experience, consist of subsets of the test levels/methods listed above.

A Smoke test (run the product, make sure it loads and runs, like a hardware smoke test where you apply power, turn it on and see if any smoke comes out…) can be made from the first steps of several different methods/levels named above. If the Smoke test is more than simply running the program once, then it should probably be some part of one of the other methods/levels. Or to put it another way, the work that goes into setting up the smoke test should be shared/captured. There might be a ..test/smoke/… directory, but the contents should be copied from somewhere else.

A Sanity test, a Stability test and Regression tests are successively larger swaths, at lower and lower levels, of the System, Performance, User Interface, Functional, etc. tests. They should be specified and are not embarrassing, but their content should be drawn from or reflected by those larger level-based tests. The should not be original and alone.

What do you think?

“Testing – How does one learn QA?” – An answer I posted on the StackOverflow “Programers” forum

up vote 1 down vote

Ziv, the questioner asks: ” … how would one proceed if he wants to learn QA? More specifically, a programmer who wants to learn about the QA process and how to manage a good QA methodology. I was given the role of jumpstarting a QA process in our company and I’m a bit lost, what are the different types of testing (system, integration, white box, black box) and which are most important to implement first? How would one implement them?“ I wrote: There are simple rules of thumb. Try what the manual says. Install and run on a clean target, user license, the works. Does it work? Did you have to add anything not covered in the manual? Are all the default control values usable? Or is there something that’s wrong, or blank, by default and always has to be changed? Set every value in the user interface to something other than its default. Can you detect a difference caused by the change? Is it correct? Do them one at a time, or in the smallest sets possible, to make the results clear. Set every value in the user interface to a second, non-default, value. Change everything at once. Can you detect the difference? Is it correct? One by one, do something to cause every error message to be generated. Do something similar, but correctly, so that no error message is generated. All of the above depend on changing a condition, between an “A” case and a “B” case, and that change having a detectable result. Then the “C” case produces another change, another result, and so forth. For 10 tests, you need 11 conditions. Using defaults as much as possible is a good first condition. By now you’ve got a list of things to test, that you recorded, and results, that you recorded, and maybe some new bugs. Throw something big and complicated at the solution. Give it a file of 173000 words to sort, paste a Jane Austin novel or some telecommunications standard 100 pages long, a 50MB bitmap graphic, 3 hours of streaming video. Open the performance monitor and get CPU-bound, or I/O bound. For an hour. Check memory use: always increasing? Rises and falls? Take the list of bugs closed in the last week, month, sprint, etc. Check them. All. Are they really fixed? Keep track of what to do, how it worked on what version/release/build/configuration, open and closed bugs, what controls have been set or changed, what data, test files or examples have been used, etc. is all part of Quality world. Keep results as tables in a spread sheet, make version controlled backups / saves. Someone writing software, or any one creating anything, has an idea of what they’re trying to make. The quality process starts with expectations. Requirements, specifications, rules, or another articulation of what’s expected. Then there’s the solution, the thing offered to perform, assist, enable or automate what’s expected. Then there are tests, operations, examples, inspections, measurements, questionnaires, etc., to relate one or more particular solution(s) to (relevant) expectations. Finally, there’s an adjustment, compensation, tuning, correction or other positive action that is hoped to affect the solution(s). When one writes software, one has a goal of it doing something, and to the extent that’s expressed, the behavior can be checked. Hello.exe displays “Hello World” on a screen. “2**150” in the Python interpreter displays, “1427247692705959881058285969449495136382746624L”. Etc. For small problems and small solutions, its possible to exhaustively test for expected results. But you wouldn’t test a word processor just by typing in some words, or even whole documents. There are limits of do-ability and reason. If you did type in all of “Emma” by Jane Austin, would you have to try her other four novels? “Don Quixote” in Spanish? Hence an emphasis on expectations. Meeting expectations tells you when the solution is complete. My web search for “Learn Quality Assurance” just returned 46 million potential links, so there’s no shortage of opinions. Classic books on the subject (my opinion, worth what you paid for it:) include “Quality is Free” by Philip Crosby, “Zen and the Art of Motorcycle Maintenance” by Robert Pursig “Managing the Software Process” by Watts Humphrey “The Mythical Man Month” by Fred Brooks “Code Complete” by Steve McConnell Take 5 minutes to read some of the Amazon reviews of those books and you’ll be on your way. Get one or more and read them. They’re not boring. Browse ASQ, Dr. Dobbs, Stack Overflow. Above all, just like writing software. DO it. Consider the quality of some software under your control. Does it meet expectation? If so, firm hand-shake and twinkle in the eye. Excellent!. If not, can it be corrected? Move to the next candidate. I like the Do-Test-Evaluate-Correct loop, but its not a Universal Truth. Pick a process and follow it consciously. Have people try the testing, verification and validation steps described in the language manual they use most frequently. Its right there on their desk, or in their phone’s browser. Look at your expectations. Are they captured in a publicly known place? With revision control? Does anyone use them? Is there any point where the solutions being produced are checked against the expectations they are supposed to be meeting? Look at your past and current bug reports. (You need a bug tracking system. If you don’t have one, start there.) What’s the most common catastrophic bug that stops shipment or requires an immediate patch? Whats the most commonly reported customer bug? What’s the most common bug that doesn’t get fixed? Take a look at ISO 9000 process rules. Reflect on value to your customers/users. Is there’s a “customer value statement” that explains how some change affects the customer’s perception of the value of the solution? How about in the requirements? By “the QA process”, you could mean “Quality Assurance”, versus “QC”, “Quality Control”? You might start with the http://www.ASQ.org web site, where the “American Society for Quality” dodges the question by not specifying “Control” (their old name was “ASQC”) or “Assurance”. Quality; alone, “assured” or “controlled”, is a big idea with multiple, overlapping definitions and usages. Some will tell you it cannot be measured in degrees- its present or not, no “high quality” or “low quality” for them. Another famous claim is that no definition is satisfactory, so its good to talk about it, but avoid being pinned down in a precise definition. How do you feel about it?   The original posting is at http://programmers.stackexchange.com/questions/255583/how-does-one-learn-qa/255595#255595

Ha! I now have a trivial python program the works interactively! momdad.py:

“””
“””

import os

print “os.listdir(os.getcwd())”
print os.listdir(os.getcwd())

for fname in os.listdir(os.getcwd()):
print fname
text=open(fname).read()
print “text.count( hi mom )”
print text.count(‘hi mom’)

A philosophic reason to sets pointers to NULL after free’ing them.

A philosophic reason to sets pointers to NULL after free’ing them.

On reflection, I think I got this one right, and I kinda like it! I have experienced other people’s double-free errors, surprisingly, but the value of crashing-on-access-via-null is persuasive to me. Making double-free happen silently and without error is a small cost for positively crashing on a rogue access through a freed pointer.

Trending up: Windows7, Agile Methodologies, Scrum, Python. Everything else? Down!

Linked-in are now listing ala-carte qualifications which one can endorse one’s acquaintances on, or be endorsed by them. No surprise, 20 people say I’m good at “hardware”, which is my highest endorsement. What I want to draw your attention to here is that if you hover your pointer over each of the possible qualifications, Linked-in will show you a working definition and the year-to-year trend on people who say they do-have-know-practice-are-qualified-in the specific item.

Not so surprising, people saying they know ‘hardware’ are down year on year… also C, C++, software engineering, Perforce, customer support, regression test, unit test, and so forth. VMware is 0% – neither up nor down over last year.

Agile Methodologies are up, Windows 7 is way-up, Python is up, Scrum is up. The other 44 categories, on my list, not including VMware at 0 and Windows 8 which doesn’t have a year on year trend, are down.

So, among people who list qualifications similar to mine, the majority and growth area are Python users, on Windows 7, employing Scrum and Agile project management methods.

Your choice whether that’s:

a) what everyone wants;

b) what people looking for work think they need;

c) some cross section of professionals on Linked-in.

I think its worth noting in passing, but not worth a lot of study. But it is a curiosity.