2 research outputs found
OSCAR. A Noise Injection Framework for Testing Concurrent Software
“Moore’s Law” is a well-known observable phenomenon in computer science that describes a
visible yearly pattern in processor’s die increase. Even though it has held true for the last 57
years, thermal limitations on how much a processor’s core frequencies can be increased, have
led to physical limitations to their performance scaling. The industry has since then shifted
towards multicore architectures, which offer much better and scalable performance, while in
turn forcing programmers to adopt the concurrent programming paradigm when designing new
software, if they wish to make use of this added performance. The use of this paradigm comes
with the unfortunate downside of the sudden appearance of a plethora of additional errors in
their programs, stemming directly from their (poor) use of concurrency techniques.
Furthermore, these concurrent programs themselves are notoriously hard to design and to
verify their correctness, with researchers continuously developing new, more effective and effi-
cient methods of doing so. Noise injection, the theme of this dissertation, is one such method. It
relies on the “probe effect” — the observable shift in the behaviour of concurrent programs upon
the introduction of noise into their routines. The abandonment of ConTest, a popular proprietary
and closed-source noise injection framework, for testing concurrent software written using the
Java programming language, has left a void in the availability of noise injection frameworks for
this programming language.
To mitigate this void, this dissertation proposes OSCAR — a novel open-source noise injection
framework for the Java programming language, relying on static bytecode instrumentation for
injecting noise. OSCAR will provide a free and well-documented noise injection tool for research,
pedagogical and industry usage. Additionally, we propose a novel taxonomy for categorizing new
and existing noise injection heuristics, together with a new method for generating and analysing
concurrent software traces, based on string comparison metrics.
After noising programs from the IBM Concurrent Benchmark with different heuristics, we
observed that OSCAR is highly effective in increasing the coverage of the interleaving space, and
that the different heuristics provide diverse trade-offs on the cost and benefit (time/coverage) of
the noise injection process.Resumo
A “Lei de Moore” é um fenómeno, bem conhecido na área das ciências da computação, que
descreve um padrão evidente no aumento anual da densidade de transístores num processador.
Mesmo mantendo-se válido nos últimos 57 anos, o aumento do desempenho dos processadores
continua garrotado pelas limitações térmicas inerentes `a subida da sua frequência de funciona-
mento. Desde então, a industria transitou para arquiteturas multi núcleo, com significativamente
melhor e mais escalável desempenho, mas obrigando os programadores a adotar o paradigma
de programação concorrente ao desenhar os seus novos programas, para poderem aproveitar o
desempenho adicional que advém do seu uso. O uso deste paradigma, no entanto, traz consigo,
por consequência, a introdução de uma panóplia de novos erros nos programas, decorrentes
diretamente da utilização (inadequada) de técnicas de programação concorrente.
Adicionalmente, estes programas concorrentes são conhecidos por serem consideravelmente
mais difíceis de desenhar e de validar, quanto ao seu correto funcionamento, incentivando investi-
gadores ao desenvolvimento de novos métodos mais eficientes e eficazes de o fazerem. A injeção
de ruído, o tema principal desta dissertação, é um destes métodos. Esta baseia-se no “efeito sonda”
(do inglês “probe effect”) — caracterizado por uma mudança de comportamento observável em
programas concorrentes, ao terem ruído introduzido nas suas rotinas. Com o abandono do Con-
Test, uma framework popular, proprietária e de código fechado, de análise dinâmica de programas
concorrentes através de injecção de ruído, escritos com recurso `a linguagem de programação Java,
viu-se surgir um vazio na oferta de framework de injeção de ruído, para esta mesma linguagem.
Para mitigar este vazio, esta dissertação propõe o OSCAR — uma nova framework de injeção de
ruído, de código-aberto, para a linguagem de programação Java, que utiliza manipulação estática
de bytecode para realizar a introdução de ruído. O OSCAR pretende oferecer uma ferramenta
livre e bem documentada de injeção de ruído para fins de investigação, pedagógicos ou até para
a indústria. Adicionalmente, a dissertação propõe uma nova taxonomia para categorizar os dife-
rentes tipos de heurísticas de injecção de ruídos novos e existentes, juntamente com um método
para gerar e analisar traces de programas concorrentes, com base em métricas de comparação de
strings.
Após inserir ruído em programas do IBM Concurrent Benchmark, com diversas heurísticas, ob-
servámos que o OSCAR consegue aumentar significativamente a dimensão da cobertura do espaço de estados de programas concorrentes. Adicionalmente, verificou-se que diferentes heurísticas
produzem um leque variado de prós e contras, especialmente em termos de eficácia versus
eficiência
Development and validation of an electronic daily control score for asthma (e-DASTHMA) : a real-world direct patient data study
Background Validated questionnaires are used to assess asthma control over the past 1-4 weeks from reporting. However, they do not adequately capture asthma control in patients with fluctuating symptoms. Using the Mobile Airways Sentinel Network for airway diseases (MASK-air) app, we developed and validated an electronic daily asthma control score (e-DASTHMA).Methods We used MASK-air data (freely available to users in 27 countries) to develop and assess different daily control scores for asthma. Data-driven control scores were developed based on asthma symptoms reported by a visual analogue scale (VAS) and self-reported asthma medication use. We included the daily monitoring data from all MASK-air users aged 16-90 years (or older than 13 years to 90 years in countries with a lower age of digital consent) who had used the app in at least 3 different calendar months and had reported at least 1 day of asthma medication use. For each score, we assessed construct validity, test-retest reliability, responsiveness, and accuracy. We used VASs on dyspnoea and work disturbance, EQ-5D-VAS, Control of Allergic Rhinitis and Asthma Test (CARAT), CARAT asthma, and Work Productivity and Activity Impairment: Allergy Specific (WPAI:AS) questionnaires as comparators. We performed an internal validation using MASK-air data from Jan 1 to Oct 12, 2022, and an external validation using a cohort of patients with physician-diagnosed asthma (the INSPIRERS cohort) who had had their diagnosis and control (Global Initiative for Asthma [GINA] classification) of asthma ascertained by a physician.Findings We studied 135 635 days of MASK-air data from 1662 users from May 21, 2015, to Dec 31, 2021. The scores were strongly correlated with VAS dyspnoea (Spearman correlation coefficient range 0.68-0.82) and moderately correlated with work comparators and quality-of-life-related comparators (for WPAI:AS work, we observed Spearman correlation coefficients of 0.59-0.68). They also displayed high test-retest reliability (intraclass correlation coefficients range 0.79-0.95) and moderate-to-high responsiveness (correlation coefficient range 0.69-0.79; effect size measures range 0.57-0.99 in the comparison with VAS dyspnoea). The best-performing score displayed a strong correlation with the effect of asthma on work and school activities in the INSPIRERS cohort (Spearman correlation coefficients 0.70; 95% CI 0.61-0.78) and good accuracy for the identification of patients with uncontrolled or partly controlled asthma according to GINA (area under the receiver operating curve 0.73; 95% CI 0.68-0.78).Interpretation e-DASTHMA is a good tool for the daily assessment of asthma control. This tool can be used as an endpoint in clinical trials as well as in clinical practice to assess fluctuations in asthma control and guide treatment optimisation.Funding None.Copyright (c) 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license.Peer reviewe