Visualization of Coupling and Programming to Interface for Object-Oriented Systems

Rosner, Peter; Viswanathan, Sree Aurovindh

doi:10.1109/iv.2008.96

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hanakawa's visualization tool presents two maps: a module coupling map and a logical coupling one. Both of them can be shown at the same time.JCTIViz[119] (2008) uses a polymetric view to display software metrics. Each class or interface is represented with a node: the dimensions of the node (height, width and depth) represents the value of a metric.…”

mentioning

confidence: 99%

A survey on software coupling relations and tools

Fregnan

Baum

Palomba

et al. 2019

Information and Software Technology

View full text Add to dashboard Cite

Context: Coupling relations reflect the dependencies between software entities and can be used to assess the quality of a program. For this reason, a vast amount of them has been developed, together with tools to compute their related metrics. However, this makes the coupling measures suitable for a given application challenging to find. Goals: The first objective of this work is to provide a classification of the different kinds of coupling relations, together with the metrics to measure them. The second consists in presenting an overview of the tools proposed until now by the software engineering academic community to extract these metrics. Method: This work constitutes a systematic literature review in software engineering. To retrieve the referenced publications, publicly available scientific research databases were used. These sources were queried using keywords inherent to software coupling. We included publications from the period 2002 to 2017 and highly cited earlier publications. A snowballing technique was used to

show abstract

mentioning

confidence: 99%

A survey on software coupling relations and tools

Fregnan

Baum

Palomba

et al. 2019

Information and Software Technology

View full text Add to dashboard Cite

show abstract

An Exploratory Framework for Intelligent Labelling of Fault Datasets

Rizwan

Habib

Sarwar

et al. 2022

2022 Human-Centered Cognitive Systems (HCCS)

View full text Add to dashboard Cite

Software fault prediction (SFP) has become a pivotal aspect in realm of software quality. Nevertheless, discipline of software quality suffers the starvation of fault datasets. Most of the research endeavors are focused on type of dataset, its granularity, metrics usedand metrics extractors. However, sporadic attention has been exerted on developmentof fault datasets and their associated challenges. There are very few publicly available datasets limiting the possibilities of comprehensive experiments on way to improvising the quality of software. Current research targets to address the challenges pertinent to fault dataset collection and developmentif one is not available publicly. It also considers dynamic identification of available resources such as public dataset, open-source software archieves, metrics parsers and intelligent labeling techniques. A framework for datasetcollection and development process has been furnished alongwith evaluation procedure for the identified resources.

show abstract

Visualization of Coupling and Programming to Interface for Object-Oriented Systems

Cited by 2 publications

References 11 publications

A survey on software coupling relations and tools

A survey on software coupling relations and tools

An Exploratory Framework for Intelligent Labelling of Fault Datasets

Contact Info

Product

Resources

About