Empirical Methods and Studies in Software Engineering: by Reidar Conradi, Alf Inge Wang

By Reidar Conradi, Alf Inge Wang

Nowadays, societies crucially rely on top quality software program for a wide a part of their functionalities and actions. as a result, software program pros, researchers, managers, and practitioners alike need to accurately make a decision what software program applied sciences and items to decide on for which purpose.

For numerous purposes, systematic empirical experiences using strictly clinical tools are infrequently practiced in software program engineering. hence there's an unquestioned want for constructing more suitable and better-qualified empirical equipment, for his or her software in perform and for dissemination of the results.

This booklet describes other forms of empirical reports and techniques for acting such experiences, e.g., for making plans, appearing, reading, and reporting such reviews. genuine stories are awarded intimately in quite a few chapters facing inspections, checking out, object-oriented concepts, and component-based software program engineering.

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Extra info for Empirical Methods and Studies in Software Engineering: Experiences from ESERNET

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G. A Controlled Experiment in Maintenance Comparing Design Patterns to Simpler Solutions. IEEE Transactions on Software Engineering, Vol. 27, No. 12, pp. 1134–1144, 2001. , Anda, B. K. An Evaluation of Applying Use Cases to Construct Design versus Validate Design, Hawaii International Conference on System Sciences (HICSS-36), Big Island, Hawaii, January 6–9, 2003. , Bunse, C. W. A Controlled Experiment for Evaluating Quality Guidelines on the Maintainability of Object-Oriented Designs, IEEE Transactions on Software Engineering, Vol.

Challenges and Recommendations 29 Most experiments in software engineering seem simplified and short-term: “the experimental variable must yield an observable effect in a matter of hours rather than six months or a year” [10]. Such experiments are hardly realistic given the tasks of building and maintaining real, industrial software, particularly since many of the factors we wish to study require significant time before we can obtain meaningful results. , a set of tasks that is a representative sample of tasks from the population of all tasks.

The experiences from using SESE are positive. SESE enables us to run distributed experiments – both in location and time – instead of only “big-bang” experiments. If acceptable from a methodological point of view, one should avoid “big-bang” experiments to reduce risks. For example, in our design pattern experiment, a fibre cable breakdown far beyond our control forced us to send 44 consultants home and defer the experiment to start on the next day. This accident caused a lot of frustration and a direct loss of 20 000 ¼.

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