Dynamic Mutant Subsumption Analysis using LittleDarwin

Many academic studies in the field of software testing rely on mutation testing to use as their comparison criteria. However, recent studies have shown that redundant mutants have a significant effect on the accuracy of their results. One solution to this problem is to use mutant subsumption to detect redundant mutants. Therefore, in order to facilitate research in this field, a mutation testing tool that is capable of detecting redundant mutants is needed. In this paper, we describe how we improved our tool, LittleDarwin, to fulfill this requirement

Evaluating Random Mutant Selection at Class-Level in Projects with Non-Adequate Test Suites

Mutation testing is a standard technique to evaluate the quality of a test suite. Due to its computationally intensive nature, many approaches have been proposed to make this technique feasible in real case scenarios. Among these approaches, uniform random mutant selection has been demonstrated to be simple and promising. However, works on this area analyze mutant samples at project level mainly on projects with adequate test suites. In this paper, we fill this lack of empirical validation by analyzing random mutant selection at class level on projects with non-adequate test suites. First, we show that uniform random mutant selection underachieves the expected results. Then, we propose a new approach named weighted random mutant selection which generates more representative mutant samples. Finally, we show that representative mutant samples are larger for projects with high test adequacy.Comment: EASE 2016, Article 11 , 10 page

A Model to Estimate First-Order Mutation Coverage from Higher-Order Mutation Coverage

The test suite is essential for fault detection during software development. First-order mutation coverage is an accurate metric to quantify the quality of the test suite. However, it is computationally expensive. Hence, the adoption of this metric is limited. In this study, we address this issue by proposing a realistic model able to estimate first-order mutation coverage using only higher-order mutation coverage. Our study shows how the estimation evolves along with the order of mutation. We validate the model with an empirical study based on 17 open-source projects.Comment: 2016 IEEE International Conference on Software Quality, Reliability, and Security. 9 page

Molecular evolution of the p53 network in reptiles

The p53 molecular network is a master regulator of how cells respond to DNA-damaging stresses. Its primary function is to respond to DNA-damage by several options: apoptosis, cellular senescence, and temporary arrest of cellular growth for DNA repair. The p53 network\u27s tight regulation of cellular fate after damage has obvious beneficial effects of preventing tumorigenesis, and possible costly effects later in life such as the accumulation of damaged cells and other aging phenotypes. Because many reptile species have evolved unique organismal stress responses, we tested the related hypothesis that the evolutionary dynamics of, and mode of selection on, genes within the p53 network differs between reptiles and mammals, and that these differences may underlie the evolution of stress response diversity. We analyzed 32 genes of the p53 network in both reptiles and mammals to compare the rates of evolutionary change and the modes of selection, (i.e., positive or purifying). We utilized transcriptomes of seventeen reptile species in order to determine protein-coding nucleotide sequences for these genes in the p53 network and performed molecular evolutionary selection analyses. We found that several genes involved in apoptosis, DNA repair and damage prevention, and inhibiting mTOR, which is an aging pathway, are undergoing different levels of selection in reptiles when compared to mammals. We discuss these findings in the context of unique adaptations to stressors found in reptiles and propose future functional research

Water Quality Monitoring Infrastructure for Tackling Water-Borne Diseases in the State of Madhya Pradesh, India, and Its Implication on the Sustainable Development Goals (SDGs)

It is estimated that around 37.7 million Indians are affected by water-borne diseases annually, 1.5 million children are estimated to die of diarrhoea alone, and 73 million working days are lost due to water-borne disease each year. The resulting economic burden is estimated at $600 million a year. Owning the largest share, India has a significant role to play in achieving global Sustainable Development Goals. In such scenario, monitoring of drinking water quality and its improvement plays a significant role in ensuring public health and reducing economic burden. Taking cue from this, a study was designed to assess the efficiency of water quality laboratories established under the National Rural Drinking Water Programme in the State of Madhya Pradesh. In the state, which tops the list of states in the country with the highest infant mortality rate (IMR), the drinking water quality assessment infrastructure is not in a position to monitor the water quality in rural areas. The study assessed that none of the 56 laboratories was able to perform a minimum of 3000 tests per year (annual analysis load) in the state for monitoring water quality. This paper presents the findings of the statewide status of water quality in rural areas and also qualitative assessment of 56 water quality laboratories in 16 districts

Cardiac transplantation in a patient with emotionally triggered implantable cardioverter defibrillator storms

The implantable cardioverter defibrillator (ICD) may be responsible for psychological disorders especially among patients experiencing multiple shocks. An associated hyperadrenergic state (e.g., anger, anxiety) may trigger malignant ventricular arrhythmias repeatedly treated by ICD shocks, entertaining a "vicious circle” often difficult to interrupt. Despite aggressive cardiac and psychological therapeutic efforts, this condition may be refractory, finally leading to heart transplantation, as described in this case repor

Improved Human Face Recognition by Introducing a New Cnn Arrangement and Hierarchical Method

Human face recognition has become one of the most attractive topics in the fields ‎of biometrics due to its wide applications. The face is a part of the body that carries ‎the most information regarding identification in human interactions. Features such ‎as the composition of facial components, skin tone, face\u27s central axis, distances ‎between eyes, and many more, alongside the other biometrics, are used ‎unconsciously by the brain to distinguish a person. Indeed, analyzing the facial ‎features could be the first method humans use to identify a person in their lives. ‎As one of the main biometric measures, human face recognition has been utilized in ‎various commercial applications over the past two decades. From banking to smart ‎advertisement and from border security to mobile applications. These are a few ‎examples that show us how far these methods have come. We can confidently say ‎that the techniques for face recognition have reached an acceptable level of ‎accuracy to be implemented in some real-life applications. However, there are other ‎applications that could benefit from improvement. Given the increasing demand ‎for the topic and the fact that nowadays, we have almost all the infrastructure that ‎we might need for our application, make face recognition an appealing topic. ‎ When we are evaluating the quality of a face recognition method, there are some ‎benchmarks that we should consider: accuracy, speed, and complexity are the main ‎parameters. Of course, we can measure other aspects of the algorithm, such as size, ‎precision, cost, etc. But eventually, every one of those parameters will contribute to ‎improving one or some of these three concepts of the method. Then again, although ‎we can see a significant level of accuracy in existing algorithms, there is still much ‎room for improvement in speed and complexity. In addition, the accuracy of the ‎mentioned methods highly depends on the properties of the face images. In other ‎words, uncontrolled situations and variables like head pose, occlusion, lighting, ‎image noise, etc., can affect the results dramatically. ‎ Human face recognition systems are used in either identification or verification. In ‎verification, the system\u27s main goal is to check if an input belongs to a pre-determined tag or a person\u27s ID. ‎Almost every face recognition system consists of four major steps. These steps are ‎pre-processing, face detection, feature extraction, and classification. Improvement ‎in each of these steps will lead to the overall enhancement of the system. In this ‎work, the main objective is to propose new, improved and enhanced methods in ‎each of those mentioned steps, evaluate the results by comparing them with other ‎existing techniques and investigate the outcome of the proposed system.