End Users’ Perspective of Performance Issues in Google Play Store Reviews

The success of mobile applications is closely tied to their performance which shapes the user experience and satisfaction. Most users often delete mobile apps from their devices due to poor performance indicating a mobile app’s failure in the competitive market. This paper performs a quantitative and qualitative analysis and investigates performance-related issues in Google Play Store reviews. This study has been conducted on 368,704 reviews emphasizing more 1- and 2-star reviews distributed over 55 Android apps. Our research also reports a taxonomy of 8 distinct performance issues obtained using manual inspection. Our findings show that end-users recurrently raised Updation (69.11%), Responsiveness (25.11%), and Network (3.28%) issues among others. These results can be used as preliminary steps towards understanding the key performance concerns from the perspective of end users. Furthermore, Our long-term objective will be to investigate whether developers resolve these performance issues in their apps.peerReviewe

A Comparative Analysis of Traditional SARIMA and Machine Learning Models for CPI Data Modelling in Pakistan

Background. In economic theory, a steady consumer price index (CPI) and its associated low inflation rate (IR) are very much preferred to a volatile one. CPI is considered a major variable in measuring the IR of a country. These indices are those of price changes and have major significance in monetary policy decisions. In this study, different conventional and machine learning methodologies have been applied to model and forecast the CPI of Pakistan. Methods. Pakistan’s yearly CPI data from 1960 to 2021 were modelled using seasonal autoregressive moving average (SARIMA), neural network autoregressive (NNAR), and multilayer perceptron (MLP) models. Several forms of the models were compared by employing the root mean square error (RMSE), mean square error (MSE), and mean absolute percentage error (MAPE) as the key performance indicators (KPIs). Results. The 20-hidden-layered MLP model appeared as the best-performing model for CPI forecasting based on the KPIs. Forecasted values of Pakistan’s CPI from 2022 to 2031 showed an astronomical increase in value which is unpleasant to consumers and economic management. Conclusion. The increasing CPI trend observed if not addressed will trigger a rising purchasing power, thereby causing higher commodity prices. It is recommended that the government put vibrant policies in place to address this alarming situation