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I have an idea for a business that requires a well designed web application. I'm not a rocket surgeon, but I'm smart enough to know that you get what you pay for and am willing to pay for talent. However, I want the development process to go as smoothly as possible and would like to know how to make that happen.
So, what information do developers need (or want) initially from the owner to avoid having to make assumptions about business (or other) requirements? Do I need to create state transition diagrams or write use cases?
Essentially, how do I take the concept in my head and package it in a way that allows the developer to do what they do best? (assuming that is creating good software. haha)
Any advice is appreciated.
Shawn
You may need to reword your question, as it is too general to get a good answer, so some vague details would be helpful.
But, the better vision you have of what you want the smoother it will be.
I find UML diagrams too confining, when you aren't going to be doing the work, as you may not come up with the best design.
So, if you start with designing out what each page should look like, as you envision it, then you can write up use cases, which are short scenarios.
So, you may write up:
A user needs to be able to log in using OpenID.
This will tell the developer one function that you want, and who you expect to do that action.
But, don't put in technologies, as you may think that a SOAP service is your best bet, but upon talking about it you may find that there is a better solution.
Use cases are good points to show what you are envisioning, and give text to your page designs.
Talk to the developers. Explain what you want and why you want it. Together you make the flow charts and whatnot. Writing requirements is part of the design process, and it's a good idea to have the developers onboard as soon as possible. Start simple and small, then grow and expand while iterating.
In talking over web services before, I have found the best starting point is drawing on a sheet of paper what you think the site will look like, and add in a few arrows from things you want clickable to the pages that should result. Keep it simple, nothing too fancy, and hopefully you and the developer can come to an understanding of what you want pretty quickly.
Use cases might be best for checking off all the points later in the project about how complete your site is; I haven't really found it to be a helpful starting point, but I'm sure others disagree. (They just seem too tedius to read when actually writing code.)
Same with state transition diagrams; they are too tedious and I think most developers will assume you made mistakes in them anyway. :) Everyone else does... Unless your project hinges very tightly on the correctness of a state machine, I wouldn't really bother.
This book contains some good advice on what constitutes a good statement of requirements from a programmers point of view. It also has the useful guideline of not trying to set the form of your requirements too early, and a substantial piece on describing the problem you are trying to solve.
I like UI mockups based on actual program/site flows e.g registering a customer or placing order. Diagrams/pictures of GUIs with structured, consistent data examples are unambiguous.
I agree that UML and use cases are only really useful if everyone speaks UML and the projects are of sufficient complexity (few are).
You may want to read up on Agile/Scrum techniques. These are becoming a sort of standard and when properly managed can save weeks of development time.
I find that words don't do a good job of communicating how a system is supposed to work. Wireframes, white-board drawings/transition diagrams, and low-fidelity prototypes are great ways to communicate a concrete idea. One example of a low-fidelity prototype is a "clickable" paper prototype that allows a user to touch "buttons" on paper to go from one drawing to another. It costs very little time (cheaper), but goes a long way to communicate an idea between two parties.
Stay away from formal documentation, UML diagrams, or class (technical documentation) diagrams that don't speak to you. This is what large, risk-averse companies move toward to be more "mature". These are also byproducts of an idea that is hashed out, and it sounds like you're in the hashing out stage.
I have been working on a course project in which we implemented an FPS using FSMs, by showing a top 2d view of the game, and using the bots and players and circles. The behaviour of bots was deterministic. For example, if the bot's health drops to below a threshhold, and the player is visible, the bot flees, else it looks for health packs.
However, I felt that in this case the bot isn't showing much of intelligence, as most of the decisions it takes are based on rules already decided by us.
What other techniques could I use, which would help me implement some real intelligence in the bot? I've been looking at HMMs, and I feel that they might help in bringing more uncertainty in the bot, and the bot might start being more autonomous in taking decisions than depending on pre defined rules.
What do you guys think? Any advice would be appreciated.
I don't think using a hidden Markov model would really be more autonomous. It would just be following the more opaque rules of the model rather than the explicit rules of the state machine. It's still deterministic. The only uncertainty they bring is to the observer, who doesn't have a simple ruleset to base predictions on.
That's not to say they can't be used effectively - if I recall correctly, several bots for FPS games used this sort of system to learn from players and develop their own AI.
But this does depend exactly on what you want to model with the process. AI is not really about algorithms, but about representation. If all you do is pick the same states that your current FSM has and observe an existing player's transitions, you're not likely to get a better system than having an expert input carefully tweaked rules for an FSM.
Given that you're not going to manage to implement "some real intelligence" as that is currently considered beyond modern science, what is it you want to be able to create? Is it a system that learns from its own experiments? A system that learns by observing human subjects? One that deliberately introduces unusual choices in order to make it harder for an opponent to predict?
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I have been working for two years in software industry. Some things that have puzzled me are as follows:
There is lack of application of mathematics in current software industry.
e.g.: When a mechanical engineer designs an electricity pole , he computes the stress on the foundation by using stress analysis techniques(read mathematical equations) to determine exactly what kind and what grade of steel should be used, but when a software developer deploys a web server application he just guesses on the estimated load on his server and leaves the rest on luck and god, there is nothing that he can use to simulate mathematically to answer his problem (my observation).
Great softwares (wind tunnel simulators etc) and computing programs(like matlab etc) are there to simulate real world problems (because they have their mathematical equations) but we in software industry still are clueless about how much actual resources in terms of memory , computing resources, clock speed , RAM etc would be needed when our server side application would actually be deployed. we just keep on guessing about the solution and solve such problem's by more or less 'hit and trial' (my observation).
Programming is done on API's, whether in c, C#, java etc. We are never able to exactly check the complexity of our code and hence efficiency because somewhere we are using an abstraction written by someone else whose source code we either don't have or we didn't have the time to check it.
e.g. If I write a simple client server app in C# or java, I am never able to calculate beforehand how much the efficiency and complexity of this code is going to be or what would be the minimum this whole client server app will require (my observation).
Load balancing and scalability analysis are just too vague and are merely solved by adding more nodes if requests on the server are increasing (my observation).
Please post answers to any of my above puzzling observations.
Please post relevant references also.
I would be happy if someone proves me wrong and shows the right way.
Thanks in advance
Ashish
I think there are a few reasons for this. One is that in many cases, simply getting the job done is more important than making it perform as well as possible. A lot of software that I write is stuff that will only be run on occasion on small data sets, or stuff where the performance implications are pretty trivial (it's a loop that does a fixed computation on each element, so it's trivially O(n)). For most of this software, it would be silly to spend time analyzing the running time in detail.
Another reason is that software is very easy to change later on. Once you've built a bridge, any fixes can be incredibly expensive, so it's good to be very sure of your design before you do it. In software, unless you've made a horrible architectural choice early on, you can generally find and optimize performance hot spots once you have some more real-world data about how it performs. In order to avoid those horrible architectural choices, you can generally do approximate, back-of-the-envelope calculations (make sure you're not using an O(2^n) algorithm on a large data set, and estimate within a factor of 10 or so how many resources you'll need for the heaviest load you expect). These do require some analysis, but usually it can be pretty quick and off the cuff.
And then there are cases in which you really, really do need to squeeze the ultimate performance out of a system. In these case, people frequently do actually sit down, work out the performance characteristics of the systems they are working with, and do very detailed analyses. See, for instance, Ulrich Drepper's very impressive paper What Every Programmer Should Know About Memory (pdf).
Think about the engineering sciences, they all have very well defined laws that are applicable to the design, and building of physical items, things like gravity, strength of materials, etc. Whereas in Computer science, there are not many well defined laws when it comes to building an application against.
I can think of many different ways to write a simple hello world program that would satisfy the requirment. However, if I have to build an electricity pole, I am severely constrained by the physical world, and the requirements of the pole.
Point by point
An electricity pole has to withstand the weather, a load, corrosion etc and these can be quantified and modelled. I can't quantify my website launch success, or how my database will grow.
Premature optimisation? Good enough is exactly that, fix it when needed. If you're a vendor, you've no idea what will be running your code in real life or how it's configured. Again you can't quantify it.
Premature optimisation
See point 1. I can add as needed.
Carrying on... even engineers bollix up. Collapsing bridges, blackout, car safety recalls, "wrong kind of snow" etc etc. Shall we change the question to "why don't engineers use more empirical observations?"
The answer to most of these is in order to have meaningful measurements (and accepted equations, limits, tolerances etc) that you have in real-world engineering you first need a way of measuring what it is that you are looking at.
Most of these things simply can't be measured easily - Software complexity is a classic, what is "complex"? How do you look at source code and decide if it is complex or not? McCabe's Cyclomatic Complexity is the closest standard we have for this but it's still basically just counting branch instructions in methods.
There is little math in software programs because the programs themselves are the equation. It is not possible to figure out the equation before it is actually run. Engineers use simple (and very complex) programs to simulate what happens in the real world. It is very difficult to simulate a simulator. additionally, many problems in computer science don't even have an answer mathematically: see traveling salesman.
Much of the mathematics is also built into languages and libraries. If you use a hash table to store data, you know to find any element can be done in constant time O(1), no matter how many elements are in the hash table. If you store it in a binary tree, it will take longer depending on the number of elements [0(n^2) if i remember correctly].
The problem is that software talks with other software, written by humans. The engineering examples you describe deal with physical phenomenon, which are constant. If I develop an electrical simulator, everyone in the world can use it. If I develop a protocol X simulator for my server, it will help me, but probably won't be worth the work.
No one can design a system from scratch and people that write semi-common libraries generally have plenty of enhancements and extensions to work on rather than writing a simulator for their library.
If you want a network traffic simulator you can find one, but it will tell you little about your server load because the traffic won't be using the protocol your server understands. Every server is going to see completely different sets of traffic.
There is lack of application of mathematics in current software industry.
e.g.: When a mechanical engineer designs an electricity pole , he computes the stress on the foundation by using stress analysis techniques(read mathematical equations) to determine exactly what kind and what grade of steel should be used, but when a software developer deploys a web server application he just guesses on the estimated load on his server and leaves the rest on luck and god, there is nothing that he can use to simulate mathematically to answer his problem (my observation).
I wouldn't say that luck or god are always the basis for load estimation. Often realistic data can be had.
It's also not true that there are no mathematical techniques to answer the question. Operations research and queuing theory can be applied to good advantage.
The real problem is that mechanical engineering is based on laws of physics and a foundation of thousands of years worth of empirical and scientific investigation. Computer science is only as old as me. Computer science will be much further along by the time your children and grandchildren apply the best practices of their day.
An MIT EE grad would not have this problem ;)
My thoughts:
Some people do actually apply math to estimate server load. The equations are very complex for many applications and many people resort to rules of thumb, guess and adjust or similar strategies. Some applications (real time applications with a high penalty for failure... weapons systems, powerplant control applications, avionics) carefully compute the required resources and ensure that they will be available at runtime.
Same as 1.
Engineers also use components provided by others, with a published interface. Think of electrical engineering. You don't usually care about the internals of a transistor, just it's interface and operating specifications. If you wanted to examine every component you use in all of it's complexity, you would be limited to what one single person can accomplish.
I have written fairly complex algorithms that determine what to scale when based on various factors such as memory consumption, CPU load, and IO. However, the most efficient solution is sometimes to measure and adjust. This is especially true if the application is complex and evolves over time. The effort invested in modeling the application mathematically (and updating that model over time) may be more than the cost of lost efficiency by try and correct approaches. Eventually, I could envision a better understanding of the correlation between code and the environment it executes in could lead to systems that predict resource usage ahead of time. Since we don't have that today, many organizations load test code under a wide range of conditions to empirically gather that information.
Software engineering are very different from the typical fields of engineering. Where "normal" engineering are bound to the context of our physical universe and the laws in it we've identified, there's no such boundary in the software world.
Producing software are usually an attempt to mirror a subset of the real-life world into a virtual reality. Here we define the laws ourselves, by only picking the ones we need and by making them just as complex as we need. Because of this fundamental difference, you need to look at the problem-solving from a different perspective. We try to make abstractions to make complex parts less complex, just like we teach kids that yellow + blue = green, when it's really the wavelength of the light that bounces on the paper that changes.
Once in a while we are bound by different laws though. Stuff like Big-O, Test-coverage, complexity-measurements, UI-measurements and the likes are all models of mathematic laws. If you look into digital signal processing, realtime programming and functional programming, you'll often find that the programmers use equations to figure out a way to do what they want. - but these techniques aren't really (to some extend) useful to create a virtual domain, that can solve complex logic, branching and interact with a user.
The reasons why wind tunnels, simulations, etc.. are needed in the engineering world is that it's much cheaper to build a scaled down prototype, than to build the full thing and then test it. Also, a failed test on a full scale bridge is destructive - you have to build a new one for each test.
In software, once you have a prototype that passes the requirements, you have the full-blown solution. there is no need to build the full-scale version. You should be running load simulations against your server apps before going live with them, but since loads are variable and often unpredictable, you're better off building the app to be able to scale to any size by adding more hardware than to target a certain load. Bridge builders have a given target load they need to handle. If they had a predicted usage of 10 cars at any given time, and then a year later the bridge's popularity soared to 1,000,000 cars per day, nobody would be surprised if it failed. But with web applications, that's the kind of scaling that has to happen.
1) Most business logic is usually broken down into decision trees. This is the "equation" that should be proofed with unit tests. If you put in x then you should get y, I don't see any issue there.
2,3) Profiling can provide some insight as to where performance issues lie. For the most part you can't say that software will take x cycles because that will change over time (ie database becomes larger, OS starts going funky, etc). Bridges for instance require constant maintenance, you can't slap one up and expect it to last 50 years without spending time and money on it. Using libraries is like not trying to figure out pi every time you want to find the circumference of a circle. It has already been proven (and is cost effective) so there is no need to reinvent the wheel.
4) For the most part web applications scale well horizontally (multiple machines). Vertical (multithreading/multiprocess) scaling tends to be much more complex. Adding machines is usually relatively easy and cost effective and avoid some bottlenecks that become limited rather easily (disk I/O). Also load balancing can eliminate the possibility of one machine being a central point of failure.
It isn't exactly rocket science as you never know how many consumers will come to the serving line. Generally it is better to have too much capacity then to have errors, pissed of customers and someone (generally your boss) chewing your hide out.
I'm interested in learning more about pattern recognition. I know that's somewhat of a broad field, so I'll list some specific types of problems I would like to learn to deal with:
Finding patterns in a seemingly random set of bytes.
Recognizing known shapes (such as circles and squares) in images.
Noticing movement patterns given a stream of positions (Vector3)
This is a new area of experimentation for me personally, and to be honest, I simply don't know where to start :-) I'm obviously not looking for the answers to be provided to me on a silver platter, but some search terms and/or online resources where I can start to acquaint myself with the concepts of the above problem domains would be awesome.
Thanks!
ps: For extra credit, if said resources provide code examples/discussion in C# would be grand :-) but doesn't need to be
Hidden Markov Models are a great place to look, as well as Artificial Neural Networks.
Edit: You could take a look at NeuronDotNet, it's open source and you could poke around the code.
Edit 2: You can also take a look at ITK, it's also open source and implements a lot of these types of algorithms.
Edit 3: Here's a pretty good intro to neural nets. It covers a lot of the basics and includes source code (albeit in C++). He implemented an unsupervised learning algorithm, I think you may be looking for a supervised backpropagation algorithm to train your network.
Edit 4: Another good intro, avoids really heavy math, but provides references to a lot of that detail at the bottom, if you want to dig into it. Includes pseudo-code, good diagrams, and a lengthy description of backpropagation.
This is kind of like saying "I'd like to learn more about electronics.. anyone tell me where to start?" Pattern Recognition is a whole field - there are hundreds, if not thousands of books out there, and any university has at least several (probably 10 or more) courses at the grad level on this. There are numerous journals dedicated to this as well, that have been publishing for decades ... conferences ..
You might start with the wikipedia.
http://en.wikipedia.org/wiki/Pattern_recognition
This is kind of an old question, but it's relevant so I figured I'd post it here :-) Stanford began offering an online Machine Learning class here - http://www.ml-class.org
OpenCV has some functions for pattern recognition in images.
You might want to look at this :http://opencv.willowgarage.com/documentation/pattern_recognition.html. (broken link: closest thing in the new doc is http://opencv.willowgarage.com/documentation/cpp/ml__machine_learning.html, although it is no longer what I'd call helpful documentation for a beginner - see other answers)
However, I also recommend starting with Matlab because openCV is not intuitive to use.
Lot of useful links on this page on computer vision related pattern recognition. Some of the links seem to be broken now but you may find it useful.
I am not an expert on this, but reading about Hidden Markov Models is a good way to start.
Beware false patterns! For any decently large data set you will find subsets that appear to have pattern, even if it is a data set of coin flips. No good process for pattern recognition should be without statistical techniques to assess confidence that the detected patterns are real. When possible, run your algorithms on random data to see what patterns they detect. These experiments will give you a baseline for the strength of a pattern that can be found in random (a.k.a "null") data. This kind of technique can help you assess the "false discovery rate" for your findings.
learning pattern-recoginition is easier in matlab..
there are several examples and there are functions to use.
it is good for the understanding concepts and experiments...
I would recommend starting with some MATLAB toolbox. MATLAB is an especially convenient place to start playing around with stuff like this due to its interactive console. A nice toolbox I personally used and really liked is PRTools (http://prtools.org); they have an implementation of pretty much every pattern recognition tool and also some other machine learning tools (Neural Networks, etc.). But the nice thing about MATLAB is that there are many other toolboxes as well you can try out (there is even a proprietary toolbox from Mathworks)
Whenever you feel comfortable enough with the different tools (and found out which classifier is perfomring best for you problem), you can start thinking about implementing the machine learning in a different application.
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How do you go about the requirements gathering phase? Does anyone have a good set of guidelines or tips to follow? What are some good questions to ask the stakeholders?
I am currently working on a new project and there are a lot of unknowns. I am in the process of coming up with a list of questions to ask the stakeholders. However I cant help but to feel that I am missing something or forgetting to ask a critical question.
You're almost certainly missing something. A lot of things, probably. Don't worry, it's ok. Even if you remembered everything and covered all the bases stakeholders aren't going to be able to give you very good, clear requirements without any point of reference. The best way to do this sort of thing is to get what you can from them now, then take that and give them something to react to. It can be a paper prototype, a mockup, version 0.1 of the software, whatever. Then they can start telling you what they really want.
See obligatory comic below...
In general, I try and get a feel for the business model my customer/client is trying to emulate with the application they want built. Are we building a glorified forms processor? Are we retrieving data from multiple sources in a single application to save time? Are we performing some kind of integration?
Once the general businesss model is established, I then move to the "must" and "must nots" for the application to dictate what data I can retrieve, who can perform what functions, etc.
Usually if you can get the customer to explain their model or workflow, you can move from there and find additional key questions.
The one question I always make sure to ask in some form or another is "What is the trickiest/most annoying thing you have to do when doing X. Typically the answer to that reveals the craziest business/data rule you'll have to implement.
Hope this helps!
Steve Yegge talks fun but there is money to be made in working out what other people's requirements are so i'd take his article with a pinch of salt.
Requirements gathering is incredibly tough because of the manner in which communication works. Its a four step process that is lossy in each step.
I have an idea in my head
I transform this into words and pictures
You interpret the pictures and words
You paint an image in your own mind of what my original idea was like
And humans fail miserably at this with worrying frequency through their adorable imperfections.
Agile does right in promoting iterative development. Getting early versions out to the client is important in identifying what features are most important (what ships in 0.1 - 0.5 ish), helps to keep you both on the right track in terms of how the application will work and quickly identifies the hidden features that you will miss.
The two main problem scenarios are the two ends of the scales:
Not having a freaking clue about what you are doing - get some domain experts
Having too many requirements - feature pit. - Question, cull (prioritise ;) ) features and use iterative development
Yegge does well in pointing out that domain experts are essential to produce good requirements because they know the business and have worked in it. They can help identify the core desire of the client and will help explain how their staff will use the system and what is important to the staff.
Alternatives and additions include trying to do the job yourself to get into the mindset or having a client staff member occasionally on-site, although the latter is unlikely to happen.
The feature pit is the other side, mostly full of failed government IT projects. Too much, too soon, not enough thought or application of realism (but what do you expect they have only about four years to make themselves feel important?). The aim here is to work out what the customer really wants.
As long as you work on getting the core components correct, efficient and bug-free clients usually remain tolerant of missing features that arrive in later shipments, as long as they eventually arrive. This is where iterative development really helps.
Remember to separate the client's ideas of what the program will be like and what they want the program to achieve.
Some clients can create confusion by communicating their requirements in the form of application features which may be poorly thought out or made redundant by much simpler functionality then they think they require. While I'm not advocating calling the client an idiot or not listening to them I feel that it is worth forever asking why they want a particular feature to get to its underlying purpose.
Remember that in either scenario it is of imperative importantance to root out the quickest path to fulfilling the customers core need and put you in a scenario where you are both profiting from the relationship.
Wow, where to start?
First, there is a set of knowledge someone should have to do analysis on some projects, but it really depends on what you are building for who. In other words, it makes a big difference if you are modifying an enterprise application for a Fortune 100 corporation, building an iPhone app, or adding functionality to a personal webpage.
Second, there are different kinds of requirements.
Objectives: What does the user want to accomplish?
Functional: What does the user need to do in order to reach their objective? (think steps to reach the objective/s)
Non-functional: What are the constraints your program needs to perform within? (think 10 vs 10k simultaneous users, growth, back-up, etc.)
Business rules: What dynamic constraints do you have to meet? (think calculations, definitions, legal concerns, etc.)
Third, the way to gather requirements most effectively, and then get feedback on them (which you will do, right?) is to use models. User cases and user stories are a model of what the user needs to do. Process models are another version of what needs to happen. System diagrams are just another model of how different parts of the program(s) interact. Good data modeling will define business concepts and show you the inputs, outputs, and changes that happen within your program. Models (and there are more than I listed) are really the key to the concern you list. A few good models will capture the needs and from models you can determine your requirements.
Fourth, get feedback. I know I mentioned this already, but you will not get everything right the first time, so get responses to what your customer wants.
As much as I appreciate requirements, and the models that drive them, users typically do not understand the ramifications of of all their requests. Constant communication with chances for review and feedback will give users a better understanding of what you are delivering. Further, they will refine their understanding based on what they see. Unless you're working for the government, iterations and / or prototypes are helpful.
First of all gather the requirements before you start coding. You can begin the design while you are gathering them depending on your project life cicle but you shouldn't ever start coding without them.
Requirements are a set of well written documents that protect both the client and yourself. Never forget that. If no requirement is present then it was not paid for (and thus it requires a formal change request), if it's present then it must be implemented and must work correctly.
Requirements must be testable. If a requirement cannot be tested then it isn't a requirement. That means something like, "The system "
Requirements must be concrete. That means stating "The system user interface shall be easy to use" is not a correct requirment.
In order to actually "gather" the requirements you need to first make sure you understand the businness model. The client will tell you what they want with its own words, it is your job to understand it and interpret it in the right context.
Make meetings with the client while you're developing the requirements. Describe them to the client with your own words and make sure you and the client have the same concept in the requirements.
Requirements require concise, testable example, but keep track of every other thing that comes up in the meetings, diagrams, doubts and try to mantain a record of every meeting.
If you can use an incremental life cycle, that will give you the ability to improve some bad gathered requirements.
You can never ask too many or "stupid" questions. The more questions you ask, the more answers you receive.
According to Steve Yegge that's the wrong question to ask. If you're gathering requirement it's already too late, your project is doomed.
High-level discussions about purpose, scope, limitations of operating environment, size, etc
Audition a single paragraph description of the system, hammer it out
Mock up UI
Formalize known requirements
Now iterate between 3 and 4 with more and more functional prototypes and more specs with more details. Write tests as you go. Do this until you have functional software and a complete, objective, testable requirements spec.
That's the dream. The reality is usually after a couple iterations everybody goes head-down and codes until there's a month left to test.
Gathering Business Requirements Are Bullshit - Steve Yegge
read the agile manifesto - working software is the only measurement for the success of a software project
get familiar with agile software practices - study Scrum , lean programming , xp etc - this will save you tremendous amount of time not only for the requirements gathering but also for the entire software development lifecycle
keep regular discussions with Customers and especially the future users and key-users
make sure you talk to the Persons understanding the problem domain - e.g. specialists in the field
Take small notes during the talks
After each CONVERSATION write an official requirement list and present it for approving. Later on it would be difficult to argue against all agreed documentation
make sure your Customers know approximately what are the approximate expenses in time and money for implementing "nice to have" requirements
make sure you label the requirements as "must have" , "should have" and "nice to have" from the very beginning, ensure Customers understand the differences between those types also
integrate all documents into the latest and final requirements analysis (or the current one for the iteration or whatever agile programming cycle you are using ... )
remember that requirements do change over the software life cycle , so gathering is one thing but managing and implementing another
KISS - keep it as simple as possible
study also the environment where the future system will reside - there are more and more technological restraints from legacy or surrounding systems , since the companies do not prefer to throw to the garbage the money they have invested for decades even if in our modern minds 20 years old code is garbage ...
Like most stages of the software development process its iteration works best.
First find out who your users are -- the XYZ dept,
Then find out where they fit into the organisation -- part of Z division,
Then find out what they do in general terms -- manage cash
Then in specific terms -- collect cash from tills, and check for till fraud.
Then you can start talking to them.
Ask what problem they want you want to solve -- you will get an answer like write a bamboozling system using OCR with shark technoligies.
Ignore that answer and ask some more questions to find out what the real problem is -- they cant read the till slips to reconcile the cash.
Agree a real solution with the users -- get a better ink ribbon supplier - or connect the electronic tills to the network and upload the logs to a central server.
Then agree in detail how they will measure the success of the project.
Then and only then propose and agree a detailed set of requirements.
I would suggest you to read Roger-Pressman's Software Engineering: A Practitioner's Approach
Before you go talking to the stakeholders/users/anyone be sure you will be able to put down the gathered information in a usefull and days-lasting way.
Use a sound-recorder if it is OK with the other person and the information is bulky.
If you heard something important and you need some reasonable time to write it down, you have two choices: ask the other person to wait a second, or say goodbye to that precious information. You wont remember it right, ask any neuro-scientist.
If you detect that a point need deeper review or that you need some document you just heard of, make sure you make a commitment with the other person to send that document or schedule another meeting with a more specific purpose. Never say "I'll remember to ask for that xls file" because in most cases you wont.
Not to long after the meeting, summarize all your notes, recordings and fresh thoughts. Just summarize it rigth. Create effective reminders for the commitments.
Again, just after the meeting, is the perfect time to understand why the gathering you just did was not as right as you thought at the end of the meeting. That's when you will be able to put down a lot of meaningful questions for another meeting.
I know the question was in the perspective of the pre-meeting, but please be aware that you can work on this matters before the meeting and end up with a much usefull, complete and quality gathering.
I've been using mind mapping (like a work breakdown structure) to help gather requirements and define the unknowns (the #1 project killer). Start at a high level and work your way down. You need to work with the sponsors, users and development team to ensure you get all the angles and don't miss anything. You can't be expected to know the entire scope of what they want without their involvement...you - as a project manager/BA - need to get them involved (most important part of the job).
There are some great ideas here already. Here are some requirements gathering principles that I always like to keep in mind:
Know the difference between the user and the customer.
The business owners that approve the shiny project are usually the customers. However, a devastating mistake is the tendency to confuse them as the user. The customer is usually the person that recognizes the need for your product, but the user is the person that will actually be using the solution (and will most likely complain later about a requirement your product did not meet).
Go to more than one person
Because we’re all human, and we tend to not remember every excruciating detail. You increase your likelihood of finding missed requirements as you talk to more people and cross-check.
Avoid specials
When a user asks for something very specific, be wary. Always question the biases and see if this will really make your product better.
Prototype
Don’t wait till launch to show what you have to the user. Do frequent prototypes (you can even call them beta versions) and get constant feedback throughout the development process. You’ll probably find more requirements as you do this.
I recently started using the concepts, standards and templates defined by the International Institute of Business Analysts organization (IIBA).
They have a pretty good BOK (Book of Knowledge) that can be downloaded from their website. They do also have a certificate.
Requirements Engineering is a bit of an art, there are lots of different ways to go about it, you really have to tailor it to your project and the stakeholders involved. A good place to start is with Requirements Engineering by Karl Wiegers:
http://www.amazon.com/Software-Requirements-Second-Pro-Best-Practices/dp/0735618798/ref=pd_bbs_sr_2?ie=UTF8&s=books&qid=1234910330&sr=8-2
and a requirements engineering process which may consist of a number of steps e.g.:
Elicitation - for the basis for discussion with the business
Analysis and Description - a technical description for the purpose of the developers
Elaboration, Clarification, Verification and Negotiation - further refinement of the requirements
Also, there are a number of ways of documenting the requirements (Use Cases, Prototypes, Specifications, Modelling Languages). Each have their advantages and disadvantages. For example prototypes are very good for elicitation of ideas from the business and discussion of ideas.
I generally find that writing a set of use cases and including wireframe prototypes works well to identify an initial set of requirements. From that point it's a continual process of working with technical people and business people to further clarify and elaborate on the requirements. Keeping track of what was initially agreed and tracking additional requirements are essential to avoid scope creep. Negotiation plays a bit part here also between the various parties as per the Broken Iron Triangle (http://www.ambysoft.com/essays/brokenTriangle.html).
IMO the most important first step is to set up a dictornary of domain-specific words. When your client says "order", what does he mean? Something he receives from his customers or something he sends to his suppliers? Or maybe both?
Find the keywords in the stakeholders' business, and let them explain those words until you comprehend their meaning in the process. Without that, you will have a hard time trying to understand the requirements.
i wrote a blog article about the approach i use:
http://pm4web.blogspot.com/2008/10/needs-analysis-for-business-websites.html
basically: questions to ask your client before building their website.
i should add this questionnaire sheet is only geared towards basic website builds - like a business web presence. totally different story if you are talking about web-based software. although some of it is still relavant (e.g. questions relating to look and feel).
LM
I prefer to keep my requirements gathering process as simple, direct and thorough as possible. You can download a sample document that I use as a template for my projects at this blog posting: http://allthingscs.blogspot.com/2011/03/documenting-software-architectural.html