Effectively Incorporating Expert Knowledge in Automated Software Remodularisation

Remodularising the components of a software system is challenging: sound design principles (e.g., coupling and cohesion) need to be balanced against developer intuition of which entities conceptually belong together. Despite this, automated approaches to remodularisation tend to ignore domain knowle...

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Veröffentlicht in:IEEE transactions on software engineering Jg. 44; H. 7; S. 613 - 630
Hauptverfasser: Hall, Mathew, Walkinshaw, Neil, McMinn, Phil
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2018
IEEE Computer Society
Schlagworte:
Acceptable noise levels
Algorithm design and analysis
Automation
Clustering algorithms
domain knowledge
set partitioning
Software
Software algorithms
Software engineering
Software remodularisation
Software systems
Solution space
ISSN:0098-5589, 1939-3520
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Abstract Remodularising the components of a software system is challenging: sound design principles (e.g., coupling and cohesion) need to be balanced against developer intuition of which entities conceptually belong together. Despite this, automated approaches to remodularisation tend to ignore domain knowledge, leading to results that can be nonsensical to developers. Nevertheless, suppling such knowledge is a potentially burdensome task to perform manually. A lot information may need to be specified, particularly for large systems. Addressing these concerns, we propose the SUpervised reMOdularisation (SUMO) approach. SUMO is a technique that aims to leverage a small subset of domain knowledge about a system to produce a remodularisation that will be acceptable to a developer. With SUMO, developers refine a modularisation by iteratively supplying corrections. These corrections constrain the type of remodularisation eventually required, enabling SUMO to dramatically reduce the solution space. This in turn reduces the amount of feedback the developer needs to supply. We perform a comprehensive systematic evaluation using 100 real world subject systems. Our results show that SUMO guarantees convergence on a target remodularisation with a tractable amount of user interaction.
AbstractList Remodularising the components of a software system is challenging: sound design principles (e.g., coupling and cohesion) need to be balanced against developer intuition of which entities conceptually belong together. Despite this, automated approaches to remodularisation tend to ignore domain knowledge, leading to results that can be nonsensical to developers. Nevertheless, suppling such knowledge is a potentially burdensome task to perform manually. A lot information may need to be specified, particularly for large systems. Addressing these concerns, we propose the SUpervised reMOdularisation (SUMO) approach. SUMO is a technique that aims to leverage a small subset of domain knowledge about a system to produce a remodularisation that will be acceptable to a developer. With SUMO, developers refine a modularisation by iteratively supplying corrections. These corrections constrain the type of remodularisation eventually required, enabling SUMO to dramatically reduce the solution space. This in turn reduces the amount of feedback the developer needs to supply. We perform a comprehensive systematic evaluation using 100 real world subject systems. Our results show that SUMO guarantees convergence on a target remodularisation with a tractable amount of user interaction.
Author McMinn, Phil
Walkinshaw, Neil
Hall, Mathew
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  organization: Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
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SubjectTerms Acceptable noise levels
Algorithm design and analysis
Automation
Clustering algorithms
domain knowledge
set partitioning
Software
Software algorithms
Software engineering
Software remodularisation
Software systems
Solution space
Title Effectively Incorporating Expert Knowledge in Automated Software Remodularisation
URI https://ieeexplore.ieee.org/document/8259332
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