Model Based Testing Market Trends and Segments 2018-2027

With an upfront investment in the development of more maintainable and reliable testing suite, model-based testing (MBT) has emerged as an integral part of software development process. The movement toward models holds substantial promise, at the same time introduces new challenges to be addressed prior to realization of MBT’s benefits.

Software systems have become part of people’s daily life. Ever-growing complexity of high requirements has posed challenges to quality assurance. Several automation tools have been used for automating test execution with the help of test scripts, as better alternative to traditionally employed error-prone and time-consuming measures. However, creating and maintaining effective test script is yet to be realized. Model-based testing facilitates making improvements in test scripts by automating and systemizing the test case design.

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Surging Visibility of Automated Testing to Drive Demand for Model-Based Solutions

Model-based testing has recently gained immense momentum in light of advanced tool support, and various innovative methodological approaches. MBT has the potential to manage and accomplish cost-effective testing tasks with higher efficiency. Numerous MBT tools have been developed for supporting MBT activities, while attributes of these tools significantly vary from one another. MBT is increasingly employed for tackling impediments in testing autonomous systems. Higher adaptability of autonomous systems makes testing of such systems a non-trivial task.

Model-based testing principles have been deemed to address problems of testing autonomous systems, as they enable verification of the system’s stability and offer a cost-effective testing. The emphasis on faster software release cycles has further led organizations to opt for automated testing, in a bid to ensure release speed and quality of products simultaneously. Accelerating testing to keep pace with the development entails the need for features that remove stubborn pain points in legacy testing solutions. Model-based solutions are therefore sought-after as they eliminate requirement for coding expertise, meanwhile curtailing maintenance overhead and preventing organizations from closing in on automation rates higher than required (=90%).

Animal Models to Gain Steady Ground

Essential need for animal research to realize progress in medical science and healthcare verticals is well deemed, thereby gaining the researchers’ support. Animal models have gradually evolved to become easy and constructive in light of rapid advancements in technological innovations. Significant surge in incidences of communicable and lifestyle diseases, coupled with hazardous effects of relevant drugs have further emphasized adoption of animal models to ensure safety of humans. Government intervention has surged in research institutions, concerning development of standard protocols appertaining to animal models. Animal models are being increasingly leveraged by researchers in basic analyses, which include development of therapeutic strategies for effective treatment of diseases.

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Complexity of living systems has amplified preference for animal models, despite availability of alternatives such as computer models, tissue cultures, and cell cultures. Developing animal models, which is deemed as an effective testing optimizer in healthcare research laboratories, is an iterative process. Despite this, progress is being made where researchers are applying novel experimental approaches for elucidating biology of certain disease indications, which in turn has led development of new, efficient animal models. Prospects of model-based testing in healthcare sector continues to be promising, with researchers focusing on using pharmacodynamics biomarkers or translational efficacy to link effectiveness witnessed in clinical results through pre-clinical analyses.

With growing criticality of a successful engineering reliable software in the automotive industry, model-based development has witnessed elucidation to reduce test case derivation efforts. These efforts are being dedicated to capture innate requirements of structured models. Key companies such as BMW have conducted an anonymous survey within their testing practitioners, to conceive model-based improvements in testing activities. New model-based derivation of test cases extends beyond automated generation of tests that addresses several challenges of practitioners. This is likely to significantly facilitate quality assurance for the automotive software.

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