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Increase in the size and complexity of the software developed has made software testing a challenging exercise. A number of testing techniques are available but they differ in terms of statement coverage, condition coverage and particularly in fault detection capabilities. The size of the test suite also differs from one technique to other. Fault that has propagated into the system inadvertently, especially into the branch statements, have severe effects as they affect the logic of the program. In this paper, an experimental evaluation of the popular branch-testing techniques (Elmendorf's method, Boolean Operator (BOR), Modified Condition/Decision Coverage (MCDC), and Reinforced Criteria/Decision Coverage (RCDC)) is presented. These techniques are evaluated on the basis of types of faults they identify, size of the test suite and their effectiveness in fault detection. For experiments, various branch statements used and referred in literature are selected. Test cases and mutants were prepared for these branch statements. Mutants were prepared by seeding single operator and operand faults into the statements. The results indicate that for a subset of fault types BOR is effective. A variant of MCDC and RCDC demonstrate better performance on the full class of faults and are only slightly worse than Elmendorf's (CEG) method test suite.

In the digital age, many countries have digitalized their judgement documents and uploaded them online. The automated classification of these documents could save lots of manpower and time. In this paper, an automatic classification method is developed for online judgement documents from Washington University School of Law Supreme Court Database (SCDB). Our approach was designed under a hybrid framework of deep learning, which couples convolution neural network (CNN) and with a recurrent neural network called bidirectional long short-term memory (BiLSTM). Firstly, the CNN architecture was optimized by genetic algorithm to generate the optimal word vector. Then, the vector was used to train the bi-LSTM for Softmax classification. The experimental results show that our model far exceeded the existing models in classification accuracy, indicating the effectiveness of integrating the genetically-modified CNN with the bi-LSTM.

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Classes in Object Oriented Systems are continuously subjected to changes and defect prone. Predicting such classes is a key research area in the field of software engineering. It is important to identify such change prone classes and defect prone classes. Identifying change prone classes can help developers to build quality software on time. Considering all the above issues, this paper covers the following key issues: 1) identification of change prone classes using various approaches 2) How changes in one class affects multiple classes associated with it. 3) Study Dependency between classes and their effects.

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Background: Cardiac injury has been described in children with both acute COVID-19 and the multisystem inflammatory syndrome in children (MIS-C). Strain has been shown to be a sensitive measure of systolic function and can be used for detecting subclinical left ventricular (LV) dysfunction. We sought to describe strain findings in both groups on initial presentation and outpatient follow up. Methods: A retrospective study analyzing echocardiograms of all patients presenting with acute COVID-19 infection and MIS-C at our institution between March 2020 and December 2020 was performed. TOMTEC software was used for strain analysis in both study groups (COVID-19 and MIS-C) and age matched healthy controls. Regional strain was obtained and comparison amongst groups was performed using the Mann-Whitney U test. Strain was compared against LV ejection fraction (EF) as measured by 5/6 area length method. Results: 45 patients (34 MIS-C and 11 COVID-19) met inclusion criteria. There was a statistically significant decrease in LV longitudinal strain (p <0.001), LV circumferential strain (p <0.001) and left atrial strain (p = 0.014) in the MIS-C group when compared to the control group. There was a statistically significant decrease in LV longitudinal strain (p = 0.028) in the acute COVID-19 group. All patients with abnormal LVEF had abnormal strain. However 14 patients (41%) in the MIS-C group and 3 (27%) in the acute COVID-19 group had preserved LVEF but abnormal strain. Abnormal strain persisted in one-third of patients in the MIS-C and acute COVID-19 groups on outpatient follow up. Conclusion: Patients with MIS-C and acute COVID-19 can develop myocardial dysfunction as seen by abnormal strain. Strain may provide an additional tool in detecting subtle myocardial dysfunction. It can be routinely employed at diagnosis and at follow up evaluation of these patients.