Pemetaan Secara Sistematis Pada Metrik Kualitas Perangkat Lunak

2015 ◽  
Vol 6 (4) ◽  
Author(s):  
Mamluatul Hani’ah ◽  
Yogi Kurniaawan ◽  
Umi Laili Yuhana
Keyword(s):  
Software Quality ◽  
Quality Measurement ◽  
Measurement Model ◽  
Quality Metrics ◽  
Systematic Mapping ◽  
Quality Metric ◽  
Current Trends ◽  
Software Quality Metrics ◽  
Software Quality Models

Abstract. Software quality assurance is one method to increase quality of software. Improvement of software quality can be measured with software quality metric. Software quality metrics are part of software quality measurement model. Currently software quality models have a very diverse types, so that software quality metrics become increasingly diverse. The various types of metrics to measure the quality of software create proper metrics selection issues to fit the desired quality measurement parameters. Another problem is the validation need to be performed on these metrics in order to obtain objective and valid results. In this paper, a systematic mapping of the software quality metric is conducted in the last nine years. This paper brings up issues in software quality metrics that can be used by other researchers. Furthermore, current trends are introduced and discussed.Keywords: Software Quality, Software Assesment, Metric Abstrak. Penjaminan kualitas suatu perangkat lunak merupakan salah satu cara meningkatkan kualitas suatu perangkat lunak. Metrik kualitas perangkat lunak merupakan bagian dari model pengukuran kualitas perangkat lunak. Model kualitas perangkat lunak memiliki jenis yang sangat beragam, sehinggga metrik kualitas perangkat lunak menjadi semakin beragam jenisnya. Beragamnya jenis metrik pengukuran kualitas perangkat lunak memberikan permasalahan pemilihan metrik yang tepat agar sesuai dengan parameter pengukuran kualitas yang diinginkan. Permasalahan yang lain adalah validasi yang harus dilakukan terhadap metrik tersebut agar diperoleh hasil yang obyektif dan valid. Dalam makalah ini akan dilakukan pemetaan sistemastis terhadap metrik pengukuran kualitas perangkat lunak pada sembilan tahun terakhir. Diharapkan dengan pemetaan sistematis akan dapat memunculkan permasalahan-permasalahan pada metrik kualitas perangkat lunak yang dapat digunakan sebagai penelitian untuk peneliti yang lain. Kata Kunci: Kualitas Perangkat Lunak, Penjaminan Perangkat Lunak, Metrik

scholarly journals Template Matching and Matrix Profile for Signal Quality Assessment of Carotid and Femoral Laser Doppler Vibrometer Signals

2022 ◽  
Vol 12 ◽  
Author(s):  
Silvia Seoni ◽  
Simeon Beeckman ◽  
Yanlu Li ◽  
Soren Aasmul ◽  
Umberto Morbiducci ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Quality Assessment ◽  
Real Time ◽  
Template Matching ◽  
Quality Metrics ◽  
Laser Doppler ◽  
Signal Quality ◽  
Laser Doppler Vibrometry ◽  
Quality Metric

Background: Laser-Doppler Vibrometry (LDV) is a laser-based technique that allows measuring the motion of moving targets with high spatial and temporal resolution. To demonstrate its use for the measurement of carotid-femoral pulse wave velocity, a prototype system was employed in a clinical feasibility study. Data were acquired for analysis without prior quality control. Real-time application, however, will require a real-time assessment of signal quality. In this study, we (1) use template matching and matrix profile for assessing the quality of these previously acquired signals; (2) analyze the nature and achievable quality of acquired signals at the carotid and femoral measuring site; (3) explore models for automated classification of signal quality.Methods: Laser-Doppler Vibrometry data were acquired in 100 subjects (50M/50F) and consisted of 4–5 sequences of 20-s recordings of skin displacement, differentiated two times to yield acceleration. Each recording consisted of data from 12 laser beams, yielding 410 carotid-femoral and 407 carotid-carotid recordings. Data quality was visually assessed on a 1–5 scale, and a subset of best quality data was used to construct an acceleration template for both measuring sites. The time-varying cross-correlation of the acceleration signals with the template was computed. A quality metric constructed on several features of this template matching was derived. Next, the matrix-profile technique was applied to identify recurring features in the measured time series and derived a similar quality metric. The statistical distribution of the metrics, and their correlates with basic clinical data were assessed. Finally, logistic-regression-based classifiers were developed and their ability to automatically classify LDV-signal quality was assessed.Results: Automated quality metrics correlated well with visual scores. Signal quality was negatively correlated with BMI for femoral recordings but not for carotid recordings. Logistic regression models based on both methods yielded an accuracy of minimally 80% for our carotid and femoral recording data, reaching 87% for the femoral data.Conclusion: Both template matching and matrix profile were found suitable methods for automated grading of LDV signal quality and were able to generate a quality metric that was on par with the signal quality assessment of the expert. The classifiers, developed with both quality metrics, showed their potential for future real-time implementation.

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