Interleaving coverage criteria oriented testing of multithreaded applications

Concurrent programs run several to thousands of processes or threads in parallel and the correctness of the outcome is critical. Successful tests for deterministic systems can not be applied to concurrent programs, because of their non-deterministic behavior. Exhaustive testing is not applicable because of the search space and testing costs. We have designed a testing algorithm that produces Sequence Covering Arrays of a concurrent program's execution segments, and tests these interleaving sequences. We provide a coverage metric that works as a measure to de ne the ratio of covered test possibilities. Our approach relies on the sequence covering arrays to cover all interleavings, while requiring least amount of testing. This thesis presents the Interleaving Coverage Criteria-oriented testing of multithreaded programs, it's utility programs to take over the control of applications to run tests and the case studies that we have done to show the efficiency of the system against exhaustive testing and its variants

Towards having a cloud of mobile devices specialized for software testing

This paper proposes a novel cloud testing platform specialized for software testing. Our novel approach aims to perform dynamic analysis on mobile application binaries, generate the model of the application, its test cases and test input sets on the run. Domain information generated via dynamic analysis and utilization of combinatorial interaction testing for test case and input set analysis will be used for improving the systems coverage capability. The system will be a self learning system in the sense that the lessons learned from testing one application will be used to test another application

Model dressing for automated exploratory testing

Automation of software testing is a complex problem with multiple facets to be handled in sync to be viable. In this work we propose two novel concepts; model dressing for automated exploratory testing. Model dressing maps an application under test to a model created for the domain of the application. In its simplest form, the domain model defines the business tasks as well as the user actions that can be carried out from the perspective of the end-user. Automated exploratory testing leverages the domain knowledge as well as the experience gained from testing applications in the same domain to test another application

Domain specific conversational intelligent agents: natural language processing in real world applications

Natural language processing (NLP) is the branch of Artificial Intelligence (AI) studies that will shape the future of computing and Human Computer Interaction (HCI). Operational complexity of a conversational intelligent agent (Chat-bot) stems from human-related, linguistic and computational aspects. In this study, we define an architectural description of a chat-bot, address the NLP problems which needs to be solved and provide our proposed solutions for a functional conversational intelligent agent. The importance of Bayesian Statistics and Data mining of domain specific text and expected expansion areas are discussed for future research in the conversational AI development

Inferring dependencies among web services with predictive and statistical analysis of system logs

Software system behaviour analysis is a challenging research problem in software engineering. The main reason for this is the lack of real data from large industrial systems. Softtech Inc. is a subsidiary of a large private bank in Turkey and this study is aimed to analyse mapping the services architecture and the software system health of a particular department at Softtech using specific software web service logs. The services that are the subject of this study consist of 196 web services related to credit and credit card application transactions from various channels. While these processes are related to similar applications, they call various web services that perform different operations in the background. Related services account for 2 million logs daily. We have conducted empirical and statistical analysis on the data, in order to infer the correlations and dependencies among the observed web services. Hypothetically, we think there are 3 types of dependencies between the web services. In our experiments, we used average response times and the number of times web services are called at specific time intervals as input data. The results suggest that they can be used for inferring that there is a dependency between two web services. In this preliminary work for dependency inference from unstructured web services' log data, we have utilized simple statistical analysis tools to derive important insight about the collection of services under our observation. The results have encouraged us to carry on with a more detailed analysis approach to further advance our research efforts