Name
Abstract | ||
|---|---|---|
Software metrics are an essential means to assess software quality. For the assessment of software quality, typically sets of complementing metrics are used since individual metrics cover only isolated quality aspects rather than a quality characteristic as a whole. The choice of the metrics within such metric sets, however, is non-trivial. Metrics may intuitively appear to be complementing, but they often are in fact non-orthogonal, i.e. the information they provide may overlap to some extent. In the past, such redundant metrics have been identified, for example, by statistical correlation methods. This paper presents, based on machine learning, a novel approach to minimise sets of metrics by identifying and removing metrics which have little effect on the overall quality assessment. To demonstrate the application of this approach, results from an experiment are provided. In this experiment, a set of metrics that is used to assess the analysability of test suites that are specified using the Testing and Test Control Notation (TTCN-3) is investigated. |
Year | Venue | Keywords |
|---|---|---|
2007 | SDL Forum | novel approach,overall quality assessment,metric set,ttcn-3 quality engineering,software metrics,quality aspect,quality characteristic,redundant metrics,individual metrics,complementing metrics,software quality,test control notation,machine learning,software metric |
Field | DocType | Volume |
Test suite,Data mining,Notation,Computer science,Session Initiation Protocol,Statistical correlation,Software metric,Software quality,TTCN-3,Quality assurance | Conference | 4745 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-74983-7 | 3 |
PageRank | References | Authors |
0.40 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edith Werner | 1 | 27 | 2.56 |
| Jens Grabowski | 2 | 618 | 73.49 |
| Helmut Neukirchen | 3 | 141 | 16.93 |
| Nils Röttger | 4 | 3 | 0.40 |
| Stephan Waack | 5 | 544 | 42.99 |
| Benjamin Zeiss | 6 | 64 | 6.89 |