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A Quality Assessment Instrument for Systematic Literature Reviews in Software Engineering

Usman Mohammed, Ali Nauman Bin, Wohlin Claes

e-Informatica Software Engineering Journal

2023

Vol. 17, nr 1

art. no. 230105

Background: Systematic literature reviews (SLRs) have become a standard practice as part of software engineering (SE) research, although their quality varies. To build on the reviews, both for future research and industry practice, they need to be of high quality. Aim: To assess the quality of SLRs in SE, we put forward an appraisal instrument for SLRs. Method: A well-established appraisal instrument from research in healthcare was used as a starting point to develop the instrument. It is adapted to SE using guidelines, checklists, and experiences from SE. The first version was reviewed by four external experts on SLRs in SE and updated based on their feedback. To demonstrate its use, the updated version was also used by the authors to assess a sample of six selected systematic literature studies. Results: The outcome of the research is an appraisal instrument for quality assessment of SLRs in SE. The instrument includes 15 items with different options to capture the quality. The instrument also supports consolidating the items into groups, which are then used to assess the overall quality of an SLR. Conclusion: The presented instrument may be helpful support for an appraiser in assessing the quality of SLRs in SE.

Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning

Usman Mohammed, Zubair Mohammed, Ahmad Zeeshan, Zaidi Monji, Ijyas Thafasal, Parayangat Muneer, Wajid Mohd, Shiblee Mohammad, Ali Syed Jaffar

Archives of Acoustics

2021

Vol. 46, No. 1

41--53

Measurement of vital signs of the human body such as heart rate, blood pressure, body temperature and respiratory rate is an important part of diagnosing medical conditions and these are usually measured using medical equipment. In this paper, we propose to estimate an important vital sign – heart rate from speech signals using machine learning algorithms. Existing literature, observation and experience suggest the existence of a correlation between speech characteristics and physiological, psychological as well as emotional conditions. In this work, we estimate the heart rate of individuals by applying machine learning based regression algorithms to Mel frequency cepstrum coefficients, which represent speech features in the spectral domain as well as the temporal variation of spectral features. The estimated heart rate is compared with actual measurement made using a conventional medical device at the time of recording speech. We obtain estimation accuracy close to 94% between the estimated and actual measured heart rate values. Binary classification of heart rate as ‘normal’ or ‘abnormal’ is also achieved with 100% accuracy. A comparison of machine learning algorithms in terms of heart rate estimation and classification accuracy is also presented. Heart rate measurement using speech has applications in remote monitoring of patients, professional athletes and can facilitate telemedicine.