Sold out

Recommender Systems for Manual Testing - An Approach to Increase the Productivity of Test Teams in Black-Box Environments

English · Paperback / Softback

Description

Read more

Software testing is an arduous and expensive activity. In the context of manual testing, any effort to reduce the test execution time and to increase defect findings is welcome. One approach is to allocate test cases according to the testers profile in a way to maximise testing productivity. However, optimising the allocation of manual test cases is not a trivial task: in large companies, test managers are responsible for allocating hundreds of test cases among several testers. We implemented 2 assignment algorithms for test case allocation and defined 3 tester profiles based on recommender systems (the same kind of system that recommends, for example, a book at Amazon.com). Our allocation systems take into account the tester's effectiveness (valid defects found in the past) and expertise (ability to run tests with certain characteristics). We performed a controlled experiment in a real industrial setting in order to compare our allocation systems to the manager's allocation and to random allocations. The findings of this research are especially useful to software testing professionals, or anyone else who wants to better understand how the manual software testing process works.

Product details

Authors Juliano Iyoda, Bren Miranda, Breno Miranda
Publisher LAP Lambert Academic Publishing
 
Languages English
Product format Paperback / Softback
Released 23.01.2012
 
EAN 9783847326403
ISBN 978-3-8473-2640-3
No. of pages 104
Dimensions 150 mm x 220 mm x 5 mm
Weight 155 g
Subject Natural sciences, medicine, IT, technology > IT, data processing > IT

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.