Classification and comparison of architecture evolution reuse knowledge—a systematic review. Issue 7 (12th February 2014)

• Record Type: Journal Article
• Authors: Ahmad, Aakash; Jamshidi, Pooyan; Pahl, Claus
• Is Part Of: Journal of software. Volume 26:Issue 7(2014:Jul.)
• Page Start: 654
• Page End: 691
• Publication Date: 2014-02-12
• Subjects: Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1643
• Languages: English
• ISSNs: 2047-7473

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <sec id="smr1643-sec-0001" sec-type="section"> <title>Context</title> <p>Architecture‐centric software evolution (ACSE) enables changes in system's structure and behaviour while maintaining a global view of the software to address evolution‐centric trade‐offs. The existing research and practices for ACSE primarily focus on <italic>design‐time evolution</italic> and <italic>runtime adaptations</italic> to accommodate changing requirements in existing architectures.</p> </sec> <sec id="smr1643-sec-0002" sec-type="section"> <title>Objectives</title> <p>We aim to <italic>identify</italic>, taxonomically <italic>classify</italic> and systematically <italic>compare</italic> the existing research focused on enabling or enhancing change reuse to support ACSE.</p> </sec> <sec id="smr1643-sec-0003" sec-type="section"> <title>Method</title> <p>We conducted a systematic literature review of 32 qualitatively selected studies and taxonomically classified these studies based on solutions that enable (i) <italic>empirical acquisition</italic> and (ii) <italic>systematic application</italic> of architecture evolution reuse knowledge (AERK) to guide ACSE.</p> </sec> <sec id="smr1643-sec-0004" sec-type="section"> <title>Results</title> <p>We identified six distinct research themes that support acquisition and application of AERK. We investigated (i) <italic>how</italic> evolution reuse knowledge is defined, classified and represented in the existing research to support ACSE and (ii) <italic>what</italic> are the existing methods, techniques and solutions to support empirical acquisition and systematic application of AERK.</p> </sec> <sec id="smr1643-sec-0005" sec-type="section"> <title>Conclusions</title> <p> <italic>Change patterns</italic> (34% of selected studies) represent a predominant solution, followed by <italic>evolution styles</italic> (25%) and <italic>adaptation strategies and policies</italic> (22%) to enable application of reuse knowledge. Empirical methods for acquisition of reuse knowledge represent 19% including <italic>pattern discovery</italic>, <italic>configuration analysis</italic>, <italic>evolution and maintenance prediction</italic> techniques (approximately 6% each). A lack of focus on empirical acquisition of reuse knowledge suggests the need of solutions with <italic>architecture change mining</italic> as a complementary and integrated phase for <italic>architecture change execution</italic>. Copyright © 2014 John Wiley & Sons, Ltd.</p> </sec> </abstract>