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Test suite prioritization technique is the process of modifying the order in which tests run to meet certain objectives. Early fault detection and maximum coverage of source code are the main objectives of testing. There are several test suite prioritization approaches that have been proposed at the maintenance phase of software development life cycle. A few works are done on prioritizing test suites that satisfy modified condition decision coverage (MC/DC) criteria which are derived for safety-critical systems. The authors know that it is mandatory to do MC/DC testing for Level A type software according to RTCA/DO178C standards. The paper aims to discuss this issue.

This paper provides a novel method to prioritize the test suites for a system that includes MC/DC criteria along with other important criteria that ensure adequate testing.

In this approach, the authors generate test suites from the input Java program using concolic testing. These test suites are utilized to measure MC/DC% by using the coverage calculator algorithm. Now, use MC/DC% and the execution time of these test suites in the basic particle swarm optimization technique with a modified objective function to prioritize the generated test suites.

The proposed approach maximizes MC/DC% and minimizes the execution time of the test suites. The effectiveness of this approach is validated by experiments on 20 moderate-sized Java programs using average percentage of fault detected metric.

A new aramid base material for use in laminates to be applied to advanced surface mount technology was developed. A new fibre based on PPDETA (Poly‐p‐phenylene/3,4'‐diphenylether terephthalamide) was found to have negative thermal and hygroscopic expansion coefficients, low ionic impurities and high affinity to epoxy and polyimide resins. The fibre was processed into fabrics and papers to be used as a base material for printed circuit boards for advanced surface mount technology. Impregnation with a new epoxy resin with high purity and high temperature resistance implemented the development of a new laminate with minimal electromigration and high dimensional stability. Thus, a new laminate was developed to be used for LCCC, PGA, COB, TAB, Flip‐Chips and other advanced surface mount technologies. Reliability of the laminate to electromigration between surface conductors, between plated‐through barrels, and between opposed conductors was found to be one of the highest available today. These types of behaviour were related to the high purity and high temperature resistance of both the reinforcement material and the resin. The short life of through‐hole plating in thermal shock was improved by the application of a new plating technology. Application to multilayer boards and laminates with a low dielectric constant is also being investigated.

The paper aims to propose a novel strategic approach, named a Scorecard‐Markov model, combining an evaluation scorecard and a hidden Markov model (HMM) for new product idea screening (NPIS) decisions.

A scorecard is constructed to evaluate new product ideas on several criteria, including customer needs, marketing strength, competency, manufacturing compatibility, and distribution channels, involving a consideration of risk buy. A HMM is then developed accordingly to predict the overall performance of new ideas in terms of success probability. To implement the model, it is trained and tested by the historical dataset of a world‐class, leading company in the power tools industry through a case study.

The approach is proven to be encouraging and meaningful. The scorecard can serve as a guide for new product idea evaluation to convert experts' linguistic judgments to quantifiable and comparable data, whereas the HMM can determine the success probability of new product ideas to support NPIS decision making based on their computed evaluation performance. The optimal cut‐off value for making either a go or kill decision on each idea can thus be determined. Concerning the case company, a go decision should be made when the probability lies in the interval [0.53, 1].

The model can prevent companies from undertaking risky and failed new product development projects. Further, it is believed that this study can assist decision makers in choosing winning new product ideas towards commercialization in an effective and certain manner, thus enhancing the new product success rate in the innovation industry.

The approach incorporating the scorecard method and HMM is novel. Illustrated by the case study, the application of this approach to NPIS decisions is confirmed to be effective.

This study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application status according to different AI techniques and propose some research directions and insights to promote its wider application.

A total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis.

The literature is divided into three themes: interpretable method, explain model behavior and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed.

This study is of vital interest to industry practitioners and regulators in safety-critical domain, as it provided a clear picture of the current status and pointed out that some AI techniques have great application potential. For those that are inherently appropriate for use in safety-critical systems, regulators can conduct in-depth studies to validate and encourage their use in the industry.

This is the first review of the application of AI in safety-critical systems in the literature. It marks the first step toward advancing AI in safety-critical domain. The paper has potential values to promote the use of the term “safety-critical” and to improve the phenomenon of literature fragmentation.