Summary Search‐based unit test generation, if effective at fault detection, can lower the cost of testing. Such techniques rely on fitness functions to guide the search. Ultimately, such functions represent test goals that approximate—but do not ensure—fault detection. The need to rely on approximations leads to two questions—can fitness functions produce effective tests and, if so, which should be used to generate tests? To answer these questions, we have assessed the fault‐detection capabilities of unit test suites generated to satisfy eight white‐box fitness functions on 597 real faults from the Defects4J database. Our analysis has found that the strongest indicators of effectiveness are a high level of code coverage over the targeted class and high satisfaction of a criterion's obligations. Consequently, the branch coverage fitness function is the most effective. Our findings indicate that fitness functions that thoroughly explore system structure should be used as primary generation objectives—supported by secondary fitness functions that explore orthogonal, supporting scenarios. Our results also provide further evidence that future approaches to test generation should focus on attaining higher coverage of private code and better initialization and manipulation of class dependencies.

Studies have revealed superior face recognition skills in females, partially due to their different eye movement strategies when encoding faces. In the current study, we utilized these slight but important differences and proposed a model that estimates the gender of the viewers and classifies them into two subgroups, males and females. An eye tracker recorded participant’s eye movements while they viewed images of faces. Regions of interest (ROIs) were defined for each face. Results showed that the gender dissimilarity in eye movements was not due to differences in frequency of fixations in the ROI s per se. Instead, it was caused by dissimilarity in saccade paths between the ROIs. The difference enhanced when saccades were towards the eyes. Females showed significant increase in transitions from other ROI s to the eyes. Consequently, the extraction of temporal transient information of saccade paths through a transition probability matrix, similar to a first order Markov chain model, significantly improved the accuracy of the gender classification results.

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The article from this special issue was previously published in Software Testing, Verification and Reliability, Volume 29, Issue 4–5, 2019. For completeness we are including the title page of the article below. The full text of the article can be read in Issue 29:4–5 on Wiley Online Library: https://onlinelibrary.wiley.com/doi/10.1002/stvr.1701

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