The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
Premkumar Devanbu - One of the best experts on this subject based on the ideXlab platform.
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quality and productivity outcomes relating to continuous integration in github
Foundations of Software Engineering, 2015Co-Authors: Bogdan Vasilescu, Premkumar Devanbu, Yue Yu, Huaimin Wang, Vladimir FilkovAbstract:Software Processes comprise many steps; coding is followed by building, integration testing, system testing, deployment, operations, among others. Software Process integration and automation have been areas of key concern in software engineering, ever since the pioneering work of Osterweil; market pressures for Agility, and open, decentralized, software development have provided additional pressures for progress in this area. But do these innovations actually help projects? Given the numerous confounding factors that can influence project performance, it can be a challenge to discern the effects of Process integration and automation. Software project ecosystems such as GitHub provide a new opportunity in this regard: one can readily find large numbers of projects in various stages of Process integration and automation, and gather data on various influencing factors as well as productivity and quality outcomes. In this paper we use large, historical data on Process Metrics and outcomes in GitHub projects to discern the effects of one specific innovation in Process automation: continuous integration. Our main finding is that continuous integration improves the productivity of project teams, who can integrate more outside contributions, without an observable diminishment in code quality.
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how and why Process Metrics are better
International Conference on Software Engineering, 2013Co-Authors: F Rahman, Premkumar DevanbuAbstract:Defect prediction techniques could potentially help us to focus quality-assurance efforts on the most defect-prone files. Modern statistical tools make it very easy to quickly build and deploy prediction models. Software Metrics are at the heart of prediction models; understanding how and especially why different types of Metrics are effective is very important for successful model deployment. In this paper we analyze the applicability and efficacy of Process and code Metrics from several different perspectives. We build many prediction models across 85 releases of 12 large open source projects to address the performance, stability, portability and stasis of different sets of Metrics. Our results suggest that code Metrics, despite widespread use in the defect prediction literature, are generally less useful than Process Metrics for prediction. Second, we find that code Metrics have high stasis; they don't change very much from release to release. This leads to stagnation in the prediction models, leading to the same files being repeatedly predicted as defective; unfortunately, these recurringly defective files turn out to be comparatively less defect-dense.
Jason L Freedman - One of the best experts on this subject based on the ideXlab platform.
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evaluation of an automated pediatric malnutrition screen using anthropometric measurements in the electronic health record a quality improvement initiative
Supportive Care in Cancer, 2020Co-Authors: Charles A Phillips, Judith Bailer, Emily Foster, Preston Dogan, Elizabeth Smith, Anne F Reilly, Jason L FreedmanAbstract:Malnutrition related to undernutrition in pediatric oncology patients is associated with worse outcomes including increased morbidity and mortality. At a tertiary pediatric center, traditional malnutrition screening practices were ineffective at identifying cancer patients at risk for undernutrition and needing nutrition consultation. To efficiently identify undernourished patients, an automated malnutrition screen using anthropometric data in the electronic health record (EHR) was implemented. The screen utilized pediatric malnutrition (undernutrition) indicators from the 2014 Consensus Statement of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition with corresponding structured EHR elements. The time periods before (January 2016–August 2017) and after (September 2017–August 2018) screen implementation were compared. Process Metrics including nutrition consults, timeliness of nutrition assessments, and malnutrition diagnoses documentation were assessed using statistical Process control charts. Outcome Metrics including change in nutritional status at least 3 months after positive malnutrition screen were assessed with the Cochran-Armitage trend test. After automated malnutrition screen implementation, all Process Metrics demonstrated center line shifts indicating special cause variation. For patient admissions with a positive screen for malnutrition of any severity level, no significant improvement in status of malnutrition was observed after 3 months (P = .13). Sub-analysis of patient admissions with screen-identified severe malnutrition noted improvement in degree of malnutrition after 3 months (P = .02). Select 2014 Consensus Statement indicators for pediatric malnutrition can be implemented as an automated screen using structured EHR data. The automated screen efficiently identifies oncology patients at risk of malnutrition and may improve clinical outcomes.
Marian Jureczko - One of the best experts on this subject based on the ideXlab platform.
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Which Process Metrics can significantly improve defect prediction models? An empirical study
Software Quality Journal, 2015Co-Authors: Lech Madeyski, Marian JureczkoAbstract:The knowledge about the software Metrics which serve as defect indicators is vital for the efficient allocation of resources for quality assurance. It is the Process Metrics, although sometimes difficult to collect, which have recently become popular with regard to defect prediction. However, in order to identify rightly the Process Metrics which are actually worth collecting, we need the evidence validating their ability to improve the product metric-based defect prediction models. This paper presents an empirical evaluation in which several Process Metrics were investigated in order to identify the ones which significantly improve the defect prediction models based on product Metrics. Data from a wide range of software projects (both, industrial and open source) were collected. The predictions of the models that use only product Metrics (simple models) were compared with the predictions of the models which used product Metrics, as well as one of the Process Metrics under scrutiny (advanced models). To decide whether the improvements were significant or not, statistical tests were performed and effect sizes were calculated. The advanced defect prediction models trained on a data set containing product Metrics and additionally Number of Distinct Committers (NDC) were significantly better than the simple models without NDC, while the effect size was medium and the probability of superiority (PS) of the advanced models over simple ones was high ( $$p=.016$$ p = . 016 , $$r=-.29$$ r = - . 29 , $$\hbox {PS}=.76$$ PS = . 76 ), which is a substantial finding useful in defect prediction. A similar result with slightly smaller PS was achieved by the advanced models trained on a data set containing product Metrics and additionally all of the investigated Process Metrics ( $$p=.038$$ p = . 038 , $$r=-.29$$ r = - . 29 , $$\hbox {PS}=.68$$ PS = . 68 ). The advanced models trained on a data set containing product Metrics and additionally Number of Modified Lines (NML) were significantly better than the simple models without NML, but the effect size was small ( $$p=.038$$ p = . 038 , $$r=.06$$ r = . 06 ). Hence, it is reasonable to recommend the NDC Process metric in building the defect prediction models.
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a review of Process Metrics in defect prediction studies
Metody Informatyki Stosowanej, 2011Co-Authors: Marian Jureczko, Lech MadeyskiAbstract:Process Metrics appear to be an effective addition to software defect prediction models usually built upon product Metrics. We present a review of research studies that investigate Process Metrics in defect prediction. The following Process Metrics are discussed: Number of Revisions, Number of Distinct Committers, Number of Modified Lines, Is New and Number of Defects in Previous Revision. We not only introduce the definitions of the aforementioned Process Metrics but also present the most important results, recent advances and the summary regarding the use of these Metrics in software defect prediction models, as well as the taxonomy of the analysed Process Metrics.
Brendan Mcgrath - One of the best experts on this subject based on the ideXlab platform.
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improving tracheostomy care in the united kingdom results of a guided quality improvement programme in 20 diverse hospitals
BJA: British Journal of Anaesthesia, 2020Co-Authors: Brendan Mcgrath, Sarah E Wallace, James P Lynch, Barbara Bonvento, Barry Coe, Anna Owen, Mike Firn, Michael Brenner, Elizabeth A EdwardsAbstract:Abstract Background Inconsistent and poorly coordinated systems of tracheostomy care commonly result in frustrations, delays, and harm. Quality improvement strategies described by exemplar hospitals of the Global Tracheostomy Collaborative have potential to mitigate such problems. This 3 yr guided implementation programme investigated interventions designed to improve the quality and safety of tracheostomy care. Methods The programme management team guided the implementation of 18 interventions over three phases (baseline/implementation/evaluation). Mixed-methods interviews, focus groups, and Hospital Anxiety and Depression Scale questionnaires defined outcome measures, with patient-level databases tracking and benchmarking Process Metrics. Appreciative inquiry, interviews, and Normalisation Measure Development questionnaires explored change barriers and enablers. Results All sites implemented at least 16/18 interventions, with the magnitude of some improvements linked to staff engagement (1536 questionnaires from 1019 staff), and 2405 admissions (1868 ICU/high-dependency unit; 7.3% children) were prospectively captured. Median stay was 50 hospital days, 23 ICU days, and 28 tracheostomy days. Incident severity score reduced significantly (n=606; P Conclusions This guided improvement programme for tracheostomy patients significantly improved the quality and safety of care, contributing rich qualitative improvement data. Patient-centred outcomes were improved along with significant efficiency and cost savings across diverse UK hospitals. Clinical trial registration IRAS-ID-206955; REC-Ref-16/LO/1196; NIHR Portfolio CPMS ID 31544.
Ales živkovic - One of the best experts on this subject based on the ideXlab platform.
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software fault prediction Metrics
Information & Software Technology, 2013Co-Authors: Danijel Radjenovic, Marjan Hericko, Richard Torkar, Ales živkovicAbstract:ContextSoftware Metrics may be used in fault prediction models to improve software quality by predicting fault location. ObjectiveThis paper aims to identify software Metrics and to assess their applicability in software fault prediction. We investigated the influence of context on Metrics' selection and performance. MethodThis systematic literature review includes 106 papers published between 1991 and 2011. The selected papers are classified according to Metrics and context properties. ResultsObject-oriented Metrics (49%) were used nearly twice as often compared to traditional source code Metrics (27%) or Process Metrics (24%). Chidamber and Kemerer's (CK) object-oriented Metrics were most frequently used. According to the selected studies there are significant differences between the Metrics used in fault prediction performance. Object-oriented and Process Metrics have been reported to be more successful in finding faults compared to traditional size and complexity Metrics. Process Metrics seem to be better at predicting post-release faults compared to any static code Metrics. ConclusionMore studies should be performed on large industrial software systems to find Metrics more relevant for the industry and to answer the question as to which Metrics should be used in a given context.