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Skip to Search Results- 2documentation
- 2duplicate bug reports
- 2information retrieval
- 2machine learning
- 2software engineering textbooks
- 2software literature
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2015
Chowdhury, S.A., Kumar, L.N., Imam, M.T., Jabbar, M.S., Sapra, V., Aggarwal, K., Hindle, Abram, Greiner, R.
The first challenge to develop an energy efficient application is to measure the application's energy consumption, which requires sophisticated hardware infrastructure and significant amounts of developers' time. Models and tools that estimate software energy consumption can save developers time,...
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2017
Borle, N.C., Feghhi, M., Stroulia, E., Greiner, R., Hindle, Abram
Testing is an integral part of the software development lifecycle, approached with varying degrees of rigor by different process models. Agile process models recommend Test Driven Development (TDD) as a key practice for reducing costs and improving code quality. The objective of this work is to...
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2017
Romansky, S., Chowdhury, S.A., Hindle, Abram, Borle, N., Greiner, R.
Inefficient mobile software kills battery life. Yet, developers lack the tools necessary to detect and solve energy bugs in software. In addition, developers are usually tasked with the creation of software features and triaging existing bugs. This means that most developers do not have the time...
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2017
Aggarwal, K., Timbers, F., Rutgers, T., Hindle, Abram, Stroulia, E., Greiner, R.
Bug deduplication, ie, recognizing bug reports that refer to the same problem, is a challenging task in the software-engineering life cycle. Researchers have proposed several methods primarily relying on information-retrieval techniques. Our work motivated by the intuition that domain knowledge...
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2015
Aggarwal, K., Rutgers, T., Timbers, F., Hindle, Abram, Greiner, R., Stroulia, E.
In previous work by Alipour et al., a methodology was proposed for detecting duplicate bug reports by comparing the textual content of bug reports to subject-specific contextual material, namely lists of software-engineering terms, such as non-functional requirements and architecture keywords....
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ECG for high-throughput screening of multiple diseases: Proof-of-concept using multi-diagnosis deep learning from population-based datasets
Download2021
Sun, W., Kalmady, S.V., Salimi, A.S., Sepehrvand, N., Ly, E., Hindle, Abram, Greiner, R., Kaul, P.
Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions. However, most of the recent success of deep learning (DL) based diagnostic predictions in...
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Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale
Download2022
Sun, W., Kalmady, S.V., Wang, Z., Salimi, A., Sepehrvand, N., Hindle, Abram, Chu, L.M., Greiner, R., Kaul, P.
Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health. Point-ofcare routine assessments such as electrocardiogram (ECG), can be used to develop prediction models for identifying individuals at risk....
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Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms
Download2023
Sun, W., Kalmady, S.V., Sepehrvand, N., Salimi, A., Nademi, Y., Bainey, K., Ezekowitz, J.A., Greiner, R., Hindle, Abram, McAlister, F.A., Sandhu, R.K., Kaul, P.
The feasibility and value of linking electrocardiogram (ECG) data to longitudinal population-level administrative health data to facilitate the development of a learning healthcare system has not been fully explored. We developed ECG-based machine learning models to predict risk of mortality...