Comprehension and change impact analysis of aspect-oriented programs through declarative reasoning

In this dissertation, we discuss an approach to support declarative reasoning over aspect-oriented programs, where the AspectJ programming language is deployed as a notable (and representative) technology. The approach is based on (i) the transformation of source code into a set of facts, and (ii) the definition and implementation of relationships and dependencies between different elements of the system into rules, stored in a Prolog database. Declarative analysis allows us to extract complex information through its rich and expressive mechanisms. Our approach has two contributions. First, it can improve the comprehension of AspectJ programs, and it can be deployed for any AspectJ-like language, like e.g. AspectC#, AspectC++. The second contribution is the provision of change impact analysis for AspectJ programs. Our method is automated and tool support is available. Expected beneficiaries of our approach include system maintainers performing tasks during the "change planning" stage of evolution

Automatic Detection and Classification of Identifier Renamings

RÉSUMÉ Le lexique du code source joue un rôle primordial dans la maintenabilité des logiciels. Un lexique pauvre peut induire à une mauvaise compréhension du programme et à l'augmentation des erreurs du logiciel. Il est donc important que les développeurs maintiennent le lexique de leur code source en renommant les identifiants afin qu'ils reflètent les concepts qu'ils expriment. Dans cette thèse, nous étudions le lexique et proposons une approche pour détecter et classifier les renommages des identifiants dans le code source. La détection des renommages est basée sur la combinaison de deux techniques: la différenciation des codes sources et l'analyse de flux de données. Tandis que le classificateur de renommage utilise une base de données ontologique et un analyseur syntaxique du langage naturel pour classer les renommages selon la taxonomie que nous avons défini. Afin d'évaluer l'exactitude et l'exhaustivité du détecteur de renommage, nous avons réalisé une étude empirique sur l’historique de cinq programmes Java open-source. Les résultats de cette étude rapportent une précision de 88% et un rappel 92%. Nous avons également mené une étude exploratoire qui analyse et discute comment les identifiants sont renommés, selon la taxonomie proposée, dans les cinq programmes Java de l’étude précédente. Les résultats de cette étude exploratoire montrent qu’il existe des renommages dans chaque dimension de notre taxonomie. Afin d’appliquer l’approche proposée aux programmes PHP, nous avons adapte notre détecteur de renommages pour prendre en compte les caractéristiques inhérentes à ces programmes. Une étude préliminaire effectuée sur trois programmes PHP montre que notre approche est applicable aux programmes PHP. Cependant, ces programmes ont des tendances de renommages différentes de celles observées dans les programmes Java. Cette thèse propose deux résultats. Tout d'abord, la détection et la classification des renommages et un outil, qui peut être utilisé pour documenter les renommages. Les développeurs seront en mesure de, par exemple, rechercher des méthodes qui font partie de l’interface de programmation car celles-ci impactent les applications clientes. Ils pourront également identifier les incohérences entre le nom et la fonctionnalité d'une entité en cas de renommage dit risqué comme lors d’un renommage vers un antonyme. Deuxièmement, les résultats de nos études nous fournissent des leçons qui constituent une base de connaissance et de conseils pouvant aider les développeurs à éviter des renommages inappropriés ou inutiles et ainsi maintenir la cohérence du lexique de leur code source.----------ABSTRACT Source code lexicon plays a paramount role in software maintainability: a poor lexicon can lead to poor comprehensibility and increase software fault-proneness. For this reason, developers should maintain their source code lexicon by renaming identifiers when they do not reflect the concepts that they should express. In this thesis, we study lexicon and propose an approach to detect and classify identifier renamings in source code. The renaming detection is based on a combination of source code differencing and data flow analysis, while the renaming classifier uses an ontological database and a natural language parser to classify renamings according to a taxonomy we define. We report a study—conducted on the evolution history of five open-source Java programs—aimed at evaluating the accuracy and completeness of the renaming detector. The study reports a precision of 88% and a recall of 92%. In addition, we report an exploratory study investigating and discussing how identifiers are renamed in the five Java programs, according to our taxonomy. Moreover, we report the challenges and applicability of the proposed approach to PHP programs and report our preliminary results of renaming detection and classification for three programs. This thesis provides two outcomes. First, the renaming detection and classification approach and tool, which can be used for documenting renamings. Developers will be able to, for example, look up methods that are part of the public API (as they impact client applications), or look for inconsistencies between the name and the implementation of an entity that underwent a high risk renaming (e.g., towards the opposite meaning). Second, pieces of actionable knowledge, based on our qualitative study of renamings, that provide advice on how to avoid some unnecessary renamings

Restructuring source code identifiers

In software engineering, maintenance cost 60% of overall project lifecycle costs of any software product. Program comprehension is a substantial part of maintenance and evolution cost and, thus, any advancement in maintenance, evolution, and program understanding will potentially greatly reduce the total cost of ownership of any software products. Identifiers are an important source of information during program understanding and maintenance. Programmers often use identifiers to build their mental models of the software artifacts. Thus, poorly-chosen identifiers have been reported in the literature as misleading and increasing the program comprehension effort. Identifiers are composed of terms, which can be dictionary words, acronyms, contractions, or simple strings. We conjecture that the use of identical terms in different contexts may increase the risk of faults, and hence maintenance effort. We investigate our conjecture using a measure combining term entropy and term context-coverage to study whether certain terms increase the odds ratios of methods to be fault-prone. We compute term entropy and context-coverage of terms extracted from identifiers in Rhino 1.4R3 and ArgoUML 0.16. We show statistically that methods containing terms with high entropy and context-coverage are more fault-prone than others, and that the new measure is only partially correlated with size. We will build on this study, and will apply summarization technique for extracting linguistic information form methods and classes. Using this information, we will extract domain concepts from source code, and propose linguistic based refactoring

Maximum-Likelihood Parameter Estimation in Terahertz Time-Domain Spectroscopy

We present a maximum-likelihood method for parameter estimation in terahertz time-domain spectroscopy. We derive the likelihood function for a parameterized frequency response function, given a pair of time-domain waveforms with known time-dependent noise amplitudes. The method provides parameter estimates that are superior to other commonly used methods and provides a reliable measure of the goodness of fit. We also develop a simple noise model that is parameterized by three dominant sources and derive the likelihood function for their amplitudes in terms of a set of repeated waveform measurements. We demonstrate the method with applications to material characterization

Auto Calibration and Optimization of Large-Scale Water Resources Systems

Water resource systems modelling have constantly been a challenge through history for human being. As the innovative methodological development is evolving alongside computer sciences on one hand, researches are likely to confront more complex and larger water resources systems due to new challenges regarding increased water demands, climate change and human interventions, socio-economic concerns, and environment protection and sustainability. In this research, an automatic calibration scheme has been applied on the Gilan's large-scale water resource model using mathematical programming. The water resource model's calibration is developed in order to attune unknown water return flows from demand sites in the complex Sefidroud irrigation network and other related areas. The calibration procedure is validated by comparing several gauged river outflows from the system in the past with model results. The calibration results are pleasantly reasonable presenting a rational insight of the system. Subsequently, the unknown optimized parameters were used in a basin-scale linear optimization model with the ability to evaluate the system's performance against a reduced inflow scenario in future. Results showed an acceptable match between predicted and observed outflows from the system at selected hydrometric stations. Moreover, an efficient operating policy was determined for Sefidroud dam leading to a minimum water shortage in the reduced inflow scenario

Prediction of breast self-examination in a sample of Iranian women: an application of the Health Belief Model

<p>Abstract</p> <p>Background</p> <p>Iranian women, many of whom live in small cities, have limited access to mammography and clinical breast examinations. Thus, breast self examination (BSE) becomes an important and necessary approach to detecting this disease in its early stages in order to limit its resultant morbidity and mortality. This study examined constructs arising from the Health Belief Model as predictors of breast self examination behavior in a sample of women living in Bandar Abbas, Iran.</p> <p>Methods</p> <p>This study was conducted in eight health centers located in Bandar Abbas, Iran. The sample consisted of 240 eligible women who were selected from referrals to the centers. The inclusion criteria were as follows: aged 30 years and over; and able to read and write Farsi. Women with breast cancer, who were pregnant, or breast feeding, were excluded from the study. Data were collected by using a self administered questionnaire which included demographic characteristics and Champion's Health Belief Model Scale. This instrument measures the concepts of disease susceptibility (3 items), seriousness (6 items), benefits (4 items), barriers (8 items) and self-efficacy (10 items).</p> <p>Results</p> <p>The subjects' mean age was 37.2 (SD = 6.1) years. Just under a third of the subjects (31.7%) had performed BSE in the past and 7.1% of them performed it at least monthly. Perceived benefits and perceived self-efficacy of the women who performed BSE were significantly higher compared with women who did not practice BSE (p < 0.03). Furthermore, perceived barriers were lower among those who had performed BSE (p < 0.001). Logistic regression analysis indicated that women who perceived fewer barriers (OR: 0.70, 95% CI: 0.63-0.77, p < 0.001) and had higher self-efficacy (OR: 1.08, 95% CI: 1.02-1.13, p = 0.003) were more likely to perform BSE (R²= 0.52).</p> <p>Conclusion</p> <p>Findings from this study indicated that perceived barriers and perceived self-efficacy could be predictors of BSE behavior among the sample of women. Therefore, BSE training programs that emphasize self-efficacy and address perceived barriers are recommended.</p

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

Background The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic; characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic; and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95$7·3 trillion (95% UI 7·2–7·4) in 2019; 293·7 times the $24·8 billion (95$43·1 billion in development assistance was provided to maintain or improve health. The pandemic led to an unprecedented increase in development assistance targeted towards health; in 2020 and 2021, $1·8 billion in DAH contributions was provided towards pandemic preparedness in LMICs, and$37·8 billion was provided for the health-related COVID-19 response. Although the support for pandemic preparedness is 12·2% of the recommended target by the High-Level Independent Panel (HLIP), the support provided for the health-related COVID-19 response is 252·2% of the recommended target. Additionally, projected spending estimates suggest that between 2022 and 2026, governments in 17 (95% UI 11–21) of the 137 LMICs will observe an increase in national government health spending equivalent to an addition of 1% of GDP, as recommended by the HLIP. Interpretation There was an unprecedented scale-up in DAH in 2020 and 2021. We have a unique opportunity at this time to sustain funding for crucial global health functions, including pandemic preparedness. However, historical patterns of underfunding of pandemic preparedness suggest that deliberate effort must be made to ensure funding is maintained

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation