Software and symmetric testing methodologies are primarily used in detecting software defects, but these testing methodologies need to be optimized to mitigate the wasting of resources. As mobile applications are becoming more prevalent in recent times, the need to have mobile applications that satisfy software quality through testing cannot be overemphasized. Testing suites and software quality assurance techniques have also become prevalent, which underscores the need to evaluate the efficacy of these tools in the testing of the applications. Mutation testing is one such technique, which is the process of injecting small changes into the software under test (SUT), thereby creating mutants. These mutants are then tested using mutation testing techniques alongside the SUT to determine the effectiveness of test suites through mutation scoring. Although mutation testing is effective, the cost of implementing it, due to the problem of equivalent mutants, is very high. Many research works gave varying solutions to this problem, but none used a standardized dataset. In this research work, we employed a standard mutant dataset tool called MutantBench to generate our data. Subsequently, an Abstract Syntax Tree (AST) was used in conjunction with a tree-based convolutional neural network (TBCNN) as our deep learning model to automate the classification of the equivalent mutants to reduce the cost of mutation testing in software testing of android applications. The result shows that the proposed model produces a good accuracy rate of 94%, as well as other performance metrics such as recall (96%), precision (89%), F1-score (92%), and Matthew’s correlation coefficients (88%) with fewer False Negatives and False Positives during testing, which is significant as it implies that there is a decrease in the risk of misclassification.

The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a cost-optimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty.

Whilst there are many attributes that need to be considered for cloud service selection, performance remains one of the most crucial aspects. Thus, we argue for a transparent cloud provider comparison framework in this study. We initiate the development of TCloud: a transparent framework for public cloud service comparison. Our framework helps prospective cloud users to decipher public cloud benchmarking data and appraise the performance of public cloud services relative to their performance goals. We carried out experiments on the real public cloud environment to implement our framework and demonstrated how prospective cloud users can use the TCloud framework in understanding how well virtualized public cloud resources meet their application requirements. Unlike previous studies, the TCloud framework presents a more realistic method of appraising the performance of virtualized resources in the public cloud. TCloud is unique in the sense that it collates public cloud benchmarking data and correlates the observed performance metrics to prospective cloud users' actual application workload requirements.

The development of the digital economy in Indonesia is increasing, including Micro, Small, and Medium Enterprises (MSMEs). The digital platform is a solution for MSMEs under threat due to the impact of the Covid-19 pandemic. Indonesia's government collaboration with private sectors has initiated some efforts to recover the national economy. These efforts included developing digital platforms such as e-commerce platforms, digital wallets, and various applications that support the distribution of goods and services that business actors have widely used. The digital platforms that MSMEs have widely used need to be further evaluated for their impact on business processes and economic recovery. This paper performed a bibliometric analysis and delivered a narrative review on developing a digital platform that can be used by MSMEs. The narrative reviews reveal some findings: 1) the digital platforms can used by MSME players, and 2) the method's effectiveness in evaluating the social impact of implementing digital platforms. Furthermore, we pointed the highlight of the digital platform for MSMEs development in Indonesia. In comparison, we also exemplified the situation in Nigeria as a benchmark. As a result, this study proposes a method that can be adopted to evaluate the social impact of implementing digital platforms by MSME players. In the next stage, we consider evaluating the social impact of digital platforms implementation by MSME players.