
Summary: If you approach someone in the street and ask for directions then, provided that person knows the way and speaks the same language as you do, it should be easy for him to help you. But often, when you try to follow the directions, you become more confused and lost. Perhaps the person giving the directions has assumed that you know about a local landmark or has forgotten to mention that there is another small street on the left before the one you are seeking. Or maybe the director has not understood your request and has sent you to a place with a similar name \dots{} there are so many reasons why the transfer of information from one person to another is fraught with difficulties. When you try to discover the requirements for any kind of product the difficulties are even more complex because the source of the requirements is not just one person, it is all of the people who are stakeholders in the project. Moreover, all of these people have their own view of what is important, along with their own experience, prejudices and views of the world. Considering the variations between your sources of requirements (stakeholders) it makes sense to have a variety of techniques for discovering the requirements. We call these as trawling techniques because, like fishing, we run a net through the organization and trap as many requirements as we can. Then, using the appropriate technique, we identify the relevant requirements (the juicy codfish) and separate them from the irrelevant (the minnows). We also look for rare and amazing fish that nobody has ever seen before. We are not just concerned with finding existing requirements, we are also concerned with generating new requirements by using techniques that encourage creativity. This paper summarizes a number of techniques that wet have found useful when trawling for requirements.
Computing methodologies and applications, requirements engineering
Computing methodologies and applications, requirements engineering
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 48 | |
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
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