Empirically-Grounded Construction of Bug Prediction and Detection Tools

There is an increasing demand on high-quality software as software bugs have an economic impact not only on software projects, but also on national economies in general. Software quality is achieved via the main quality assurance activities of testing and code reviewing. However, these activities are expensive, thus they need to be carried out efficiently. Auxiliary software quality tools such as bug detection and bug prediction tools help developers focus their testing and reviewing activities on the parts of software that more likely contain bugs. However, these tools are far from adoption as mainstream development tools. Previous research points to their inability to adapt to the peculiarities of projects and their high rate of false positives as the main obstacles of their adoption. We propose empirically-grounded analysis to improve the adaptability and efficiency of bug detection and prediction tools. For a bug detector to be efficient, it needs to detect bugs that are conspicuous, frequent, and specific to a software project. We empirically show that the null-related bugs fulfill these criteria and are worth building detectors for. We analyze the null dereferencing problem and find that its root cause lies in methods that return null. We propose an empirical solution to this problem that depends on the wisdom of the crowd. For each API method, we extract the nullability measure that expresses how often the return value of this method is checked against null in the ecosystem of the API. We use nullability to annotate API methods with nullness annotation and warn developers about missing and excessive null checks. For a bug predictor to be efficient, it needs to be optimized as both a machine learning model and a software quality tool. We empirically show how feature selection and hyperparameter optimizations improve prediction accuracy. Then we optimize bug prediction to locate the maximum number of bugs in the minimum amount of code by finding the most cost-effective combination of bug prediction configurations, i.e., dependent variables, machine learning model, and response variable. We show that using both source code and change metrics as dependent variables, applying feature selection on them, then using an optimized Random Forest to predict the number of bugs results in the most cost-effective bug predictor. Throughout this thesis, we show how empirically-grounded analysis helps us achieve efficient bug prediction and detection tools and adapt them to the characteristics of each software project

Against the Mainstream in Bug Prediction

Bug prediction is a technique used to estimate the most bug-prone entities in software systems. Bug prediction approaches vary in many design options, such as dependent variables, independent variables, and machine learning models. Choosing the right combination of design options to build an effective bug predictor is hard. Previous studies do not consider this complexity and draw conclusions based on fewer-than-necessary experiments. We argue that each software project is unique from the perspective of its development process. Consequently, metrics and AI models perform differently on different projects, in the context of bug prediction. We confirm our hypothesis empirically by running different bug pre- dictors on different systems. We show that no single bug prediction configuration works globally on all projects and, thus, previous bug prediction findings cannot generalize

An extensive analysis of efficient bug prediction configurations

Background: Bug prediction helps developers steer maintenance activities towards the buggy parts of a software. There are many design aspects to a bug predictor, each of which has several options, i.e., software metrics, machine learning model, and response variable. Aims: These design decisions should be judiciously made because an improper choice in any of them might lead to wrong, misleading, or even useless results. We argue that bug prediction con?gurations are intertwined and thus need to be evaluated in their entirety, in contrast to the common practice in the ?eld where each aspect is investigated in isolation. Method: We use a cost-aware evaluation scheme to evaluate 60 di?erent bug prediction con?guration combinations on ?ve open source Java projects. Results:We ?nd out that the best choices for building a cost-e?ective bug predictor are change metrics mixed with source code metrics as independent variables, Random Forest as the machine learning model, and the number of bugs as the response variable. Combining these con?guration options results in the most e?cient bug predictor across all subject systems. Conclusions: We demonstrate a strong evidence for the interplay among bug prediction con?gurations and provide concrete guidelines for researchers and practitioners on how to build and evaluate e?cient bug predictors

Parallel computing for artificial neural network training

The big-data is an oil of this century. A high amount of computational power is required to get knowledge from data. Parallel and distributed computing is essential to processing a large amount of data. Artificial Neural Networks (ANNs) need as much as possible data to have high accuracy, whereas parallel processing can help us to save time in ANNs training. In this paper, we have implemented exemplary parallelization of neural network training by dint of Java and its native socket libraries. During the experiments, we have noticed that Java implementation tends to have memory issues when a large amount of training data sets are involved in training. We have remarked that exemplary parallelization of a neural network training will not outperform drastically when additional nodes are introduced into the system after a certain point. This is widely due to network communication complexity in the system

New proposed spherical slotted antenna covered by the layers of dielectric material and plasma

The operation of the new proposed spherical slotted antenna covered by layers of dielectric material and plasma was analyzed numerically in this paper. By utilizing the Integra-functional equations method, the optimum thickness of dielectric material layer and suitable conditions which improve the operation of this antenna are analyzed here by MATHCAD. The thickness of dielectric layer must not be less or more than λ/6. Furthermore, the authors propose manipulating the operation frequency to enable such antenna to work in most circumstances

Mining frequent bug-fix code changes

Detecting bugs as early as possible plays an important role in ensuring software quality before shipping. We argue that mining previous bug fixes can produce good knowledge about why bugs happen and how they are fixed. In this paper, we mine the change history of 717 open source projects to extract bug-fix patterns. We also manually inspect many of the bugs we found to get insights into the contexts and reasons behind those bugs. For instance, we found out that missing null checks and missing initializations are very recurrent and we believe that they can be automatically detected and fixed

Prevalence of prenatal zinc deficiency and its association with socio-demographic, dietary and health care related factors in Rural Sidama, Southern Ethiopia: A cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Several studies witnessed that prenatal zinc deficiency (ZD) predisposes to diverse pregnancy complications. However, scientific evidences on the determinants of prenatal ZD are scanty and inconclusive. The purpose of the present study was to assess the prevalence and determinants of prenatal ZD in Sidama zone, Southern Ethiopia.</p> <p>Methods</p> <p>A community based, cross-sectional study was conducted in Sidama zone in January and February 2011. Randomly selected 700 pregnant women were included in the study. Data on potential determinants of ZD were gathered using a structured questionnaire. Serum zinc concentration was measured using Atomic Absorption Spectrometry. Statistical analysis was done using logistic regression and linear regression.</p> <p>Results</p> <p>The mean serum zinc concentration was 52.4 (+/-9.9) μg/dl (95% CI: 51.6-53.1 μg/dl). About 53.0% (95% CI: 49.3-56.7%) of the subjects were zinc deficient. The majority of the explained variability of serum zinc was due to dietary factors like household food insecurity level, dietary diversity and consumption of animal source foods. The risk of ZD was 1.65 (95% CI: 1.02-2.67) times higher among women from maize staple diet category compared to <it>Enset </it>staple diet category. Compared to pregnant women aged 15-24 years, those aged 25-34 and 35-49 years had 1.57 (95% CI: 1.04-2.34) and 2.18 (95% CI: 1.25-3.63) times higher risk of ZD, respectively. Women devoid of self income had 1.74 (95% CI: 1.11-2.74) time increased risk than their counterparts. Maternal education was positively associated to zinc status. Grand multiparas were 1.74 (95% CI: 1.09-3.23) times more likely to be zinc deficient than nulliparas. Frequency of coffee intake was negatively association to serum zinc level. Positive association was noted between serum zinc and hemoglobin concentrations. Altitude, history of iron supplementation, maternal workload, physical access to health service, antenatal care and nutrition education were not associated to zinc status.</p> <p>Conclusion</p> <p>ZD is of public health concern in the area. The problem must be combated through a combination of short, medium and long-term strategies. This includes the use of household based phytate reduction food processing techniques, agricultural based approaches and livelihood promotion strategies.</p

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Higher COVID-19 pneumonia risk associated with anti-IFN-α than with anti-IFN-ω auto-Abs in children

We found that 19 (10.4%) of 183 unvaccinated children hospitalized for COVID-19 pneumonia had autoantibodies (auto-Abs) neutralizing type I IFNs (IFN-alpha 2 in 10 patients: IFN-alpha 2 only in three, IFN-alpha 2 plus IFN-omega in five, and IFN-alpha 2, IFN-omega plus IFN-beta in two; IFN-omega only in nine patients). Seven children (3.8%) had Abs neutralizing at least 10 ng/ml of one IFN, whereas the other 12 (6.6%) had Abs neutralizing only 100 pg/ml. The auto-Abs neutralized both unglycosylated and glycosylated IFNs. We also detected auto-Abs neutralizing 100 pg/ml IFN-alpha 2 in 4 of 2,267 uninfected children (0.2%) and auto-Abs neutralizing IFN-omega in 45 children (2%). The odds ratios (ORs) for life-threatening COVID-19 pneumonia were, therefore, higher for auto-Abs neutralizing IFN-alpha 2 only (OR [95% CI] = 67.6 [5.7-9,196.6]) than for auto-Abs neutralizing IFN-. only (OR [95% CI] = 2.6 [1.2-5.3]). ORs were also higher for auto-Abs neutralizing high concentrations (OR [95% CI] = 12.9 [4.6-35.9]) than for those neutralizing low concentrations (OR [95% CI] = 5.5 [3.1-9.6]) of IFN-omega and/or IFN-alpha 2