A Survey of Flaky Tests

Tests that fail inconsistently, without changes to the code under test, are described as flaky . Flaky tests do not give a clear indication of the presence of software bugs and thus limit the reliability of the test suites that contain them. A recent survey of software developers found that 59% claimed to deal with flaky tests on a monthly, weekly, or daily basis. As well as being detrimental to developers, flaky tests have also been shown to limit the applicability of useful techniques in software testing research. In general, one can think of flaky tests as being a threat to the validity of any methodology that assumes the outcome of a test only depends on the source code it covers. In this article, we systematically survey the body of literature relevant to flaky test research, amounting to 76 papers. We split our analysis into four parts: addressing the causes of flaky tests, their costs and consequences, detection strategies, and approaches for their mitigation and repair. Our findings and their implications have consequences for how the software-testing community deals with test flakiness, pertinent to practitioners and of interest to those wanting to familiarize themselves with the research area.

Emerging Advances of Detection Strategies for Tumor-Derived Exosomes

Exosomes derived from tumor cells contain various molecular components, such as proteins, RNA, DNA, lipids, and carbohydrates. These components play a crucial role in all stages of tumorigenesis and development. Moreover, they reflect the physiological and pathological status of parental tumor cells. Recently, tumor-derived exosomes have become popular biomarkers for non-invasive liquid biopsy and the diagnosis of numerous cancers. The interdisciplinary significance of exosomes research has also attracted growing enthusiasm. However, the intrinsic nature of tumor-derived exosomes requires advanced methods to detect and evaluate the complex biofluid. This review analyzes the relationship between exosomes and tumors. It also summarizes the exosomal biological origin, composition, and application of molecular markers in clinical cancer diagnosis. Remarkably, this paper constitutes a comprehensive summary of the innovative research on numerous detection strategies for tumor-derived exosomes with the intent of providing a theoretical basis and reference for early diagnosis and clinical treatment of cancer.

COVID-19 Patient Detection Based on Fusion of Transfer Learning and Fuzzy Ensemble Models Using CXR Images

The COVID-19 pandemic has claimed the lives of millions of people and put a significant strain on healthcare facilities. To combat this disease, it is necessary to monitor affected patients in a timely and cost-effective manner. In this work, CXR images were used to identify COVID-19 patients. We compiled a CXR dataset with equal number of 2313 COVID positive, pneumonia and normal CXR images and utilized various transfer learning models as base classifiers, including VGG16, GoogleNet, and Xception. The proposed methodology combines fuzzy ensemble techniques, such as Majority Voting, Sugeno Integral, and Choquet Fuzzy, and adaptively combines the decision scores of the transfer learning models to identify coronavirus infection from CXR images. The proposed fuzzy ensemble methods outperformed each individual transfer learning technique and several state-of-the-art ensemble techniques in terms of accuracy and prediction. Specifically, VGG16 + Choquet Fuzzy, GoogleNet + Choquet Fuzzy, and Xception + Choquet Fuzzy achieved accuracies of 97.04%, 98.48%, and 99.57%, respectively. The results of this work are intended to help medical practitioners achieve an earlier detection of coronavirus compared to other detection strategies, which can further save millions of lives and advantageously influence society.

An analytical and probabilistic model with concordance for detecting mine-like objects with mirror symmetry

PurposeThe purpose is to develop search and detection strategies that maximize the probability of detection of mine-like objects.Design/methodology/approachThe author have developed a methodology that incorporates variational calculus, number theory and algebra to derive a globally optimal strategy that maximizes the expected probability of detection.FindingsThe author found a set of look angles that globally maximize the probability of detection for a general class of mirror symmetric targets.Research limitations/implicationsThe optimal strategies only maximize the probability of detection and not the probability of identification.Practical implicationsIn the context of a search and detection operation, there is only a limited time to find the target before life is lost; hence, improving the chance of detection will in real terms be translated into the difference between success or failure, life or death. This rich field of study can be applied to mine countermeasure operations to make sure that the areas of operations are free of mines so that naval operations can be conducted safely.Originality/valueThere are two novel elements in this paper. First, the author determine the set of globally optimal look angles that maximize the probability of detection. Second, the author introduce the phenomenon of concordance between sensor images.