Early Stage Software Reliability Estimation with Stochastic Reward Nets

This paper presents software reliability modeling issues at the early stage of a software development for fault tolerant software management system. Based on Stochastic Reward Nets, an effective model of hierarchical view for a fault tolerant software management system is put forward, and an approach that consists of system transient performance analysis is adopted. A quantitative approach for software reliability analysis is given. The results show its usefulness for the design and evaluation of the early-stage software reliability modeling when failure data is not available.

A component-based back-propagation reliability model with low complexity for complex software systems

Since most of the available component-based software reliability models consume high computational cost and suffer from the evaluating complexity for the software system with complex structures,a component-based back-propagation reliability model(CBPRM)with low complexity for the complex software system reliability evaluation is presented in this paper.The proposed model is based on the artificial neural networks and the component reliability sensitivity analyses.These analyses are performed dynamically and assigned to the neurons to optimize the reliability evaluation.CBPRM has a linear increasing complexity and outperforms the state-based and the path-based reliability models.Another advantage of CBPRM over others is its robustness.CBPRM depends on the component reliabilities and the correlative sensitivities,which are independent from the software system structure.Based on the theory analysis and experiment results,it shows that the complexity of CBPRM is evidently lower than the contrast models and the reliability evaluating accuracy is acceptable when the software system structure is complex.

Evaluating reverse docking on general and selective inhibitors: a case study about glide

As a powerful tool for target prediction,reverse docking remains largely unexplored.The objective evaluation of reverse docking software can help us know better about the strength and weakness of these tools,hence guiding us in target prediction.In the present study,we evaluated the target prediction power of Glide(SP)against general inhibitors and selective inhibitors.The results showed that the scoring tendency could be different for each ligand,and overall scoring sampling was necessary for a better understanding of the docking score for a certain protein-ligand pair.Besides,the input conformation of the binding pocket could affect the docking result.Glide(SP)showed a preferable performance on the target prediction of the general inhibitors.However,the accuracy of the target prediction of the selective inhibitors was relatively low,indicating that Glide(SP)might not be capable for this task.The case study about COVID-19 proved that coagulation factor Xa might be a potential target of chloroquine.Therefore,we recommend the further development of reverse docking tools and rectification of inter-target scoring bias.