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https://doi.org/10.1109/icstw....
Article . 2011 . Peer-reviewed
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Automated Refactoring for Testability

Authors: Mel Ó Cinnéide; Dermot Boyle; Iman Hemati Moghadam;

Automated Refactoring for Testability

Abstract

Current software practice places a strong emphasis on unit testing, to the extent that the amount of test code produced on a project can exceed the amount of actual application code required. This illustrates the importance of testability as a feature of software. In this paper we investigate whether it is possible to improve a program's testability using an automated refactoring approach. We conduct a quasi-experiment where we create a small application that scores poorly using a proven cohesion metric, LSCC. Using our automated refactoring platform, Code-Imp, this application is automatically refactored using the LSCC metric to guide the search for better solutions. To evaluate the results, a number of industrial software engineers were asked to write test cases for the application both before and after refactoring and compare the relative difficulty involved. The results were interesting though inconclusive, and suggest that further work is required.

Country
Ireland
Keywords

LSCC metric, software, testability

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
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).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
18
Top 10%
Top 10%
Top 10%
Green