On the Impact of Order Information in API Usage Patterns

2019 ◽  
pp. 79-103
Author(s):  
Ervina Çergani ◽  
Mira Mezini
Keyword(s):  
Order Information ◽  
Usage Patterns ◽  
Api Usage ◽  
The Impact

scholarly journals Guided pattern mining for API misuse detection by change-based code analysis

2021 ◽  
Vol 28 (2) ◽  
Author(s):  
Sebastian Nielebock ◽  
Robert Heumüller ◽  
Kevin Michael Schott ◽  
Frank Ortmeier
Keyword(s):  
Pattern Mining ◽  
Third Party ◽  
Just In Time ◽  
False Alarms ◽  
Misuse Detection ◽  
Code Search ◽  
Usage Patterns ◽  
Development Processes ◽  
Code Changes ◽  
Api Usage

AbstractLack of experience, inadequate documentation, and sub-optimal API design frequently cause developers to make mistakes when re-using third-party implementations. Such API misuses can result in unintended behavior, performance losses, or software crashes. Therefore, current research aims to automatically detect such misuses by comparing the way a developer used an API to previously inferred patterns of the correct API usage. While research has made significant progress, these techniques have not yet been adopted in practice. In part, this is due to the lack of a process capable of seamlessly integrating with software development processes. Particularly, existing approaches do not consider how to collect relevant source code samples from which to infer patterns. In fact, an inadequate collection can cause API usage pattern miners to infer irrelevant patterns which leads to false alarms instead of finding true API misuses. In this paper, we target this problem (a) by providing a method that increases the likelihood of finding relevant and true-positive patterns concerning a given set of code changes and agnostic to a concrete static, intra-procedural mining technique and (b) by introducing a concept for just-in-time API misuse detection which analyzes changes at the time of commit. Particularly, we introduce different, lightweight code search and filtering strategies and evaluate them on two real-world API misuse datasets to determine their usefulness in finding relevant intra-procedural API usage patterns. Our main results are (1) commit-based search with subsequent filtering effectively decreases the amount of code to be analyzed, (2) in particular method-level filtering is superior to file-level filtering, (3) project-internal and project-external code search find solutions for different types of misuses and thus are complementary, (4) incorporating prior knowledge of the misused API into the search has a negligible effect.

scholarly journals MAPO: Mining and Recommending API Usage Patterns

2009 ◽  
pp. 318-343 ◽  
Author(s):  
Hao Zhong ◽  
Tao Xie ◽  
Lu Zhang ◽  
Jian Pei ◽  
Hong Mei
Keyword(s):  
Usage Patterns ◽  
Api Usage

scholarly journals Impact Evaluation of an SMS Campaign to Promote Household Chlorination in Rural Haiti

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3095
Author(s):  
Michael Ritter ◽  
Eveline Camille ◽  
Christophe Velcine ◽  
Rose-Kerline Guillaume ◽  
Jean Marcel Casimir ◽  
...  
Keyword(s):  
Cost Effective ◽  
Target Population ◽  
Purchase Behavior ◽  
Household Water Treatment ◽  
Behavior Change Interventions ◽  
Message Service ◽  
Usage Patterns ◽  
Mobile Phone Usage ◽  
Sales Agent ◽  
The Impact

Despite documented health benefits of household water treatment and storage (HWTS), achieving sustained use remains challenging. In prior evaluations of a long-term HWTS program in Haiti, multiple marketing interventions failed to increase use or had prohibitively high costs. Using mobile phones is a potentially cost-effective way to change HWTS behavior. We conducted a randomized experiment to evaluate the impact of sending short-message service (SMS) messages to promote household chlorination in this program in Haiti. Households (n = 1327) were randomly assigned to: One of four SMS frequencies; one of ten behavioral constructs; “cholera” or “disease” framing; and one or zero household visits from a sales agent. During the three-month campaign, there were no statistically significant relationships between the four outcomes related to chlorine purchases and any SMS frequency, any behavioral construct, or either “cholera” or “disease” framing. Receiving one visit increased the probability of purchasing a bottle of chlorine by 17.1 percentage points (p < 0.001) but did not affect subsequent purchase behavior. Costs of managing the SMS campaign were higher than expected. SMS campaigns may not be cost-effective behavior change interventions in certain contexts. If pursued, we recommend simple interventions, timed with the target behavior, and tailored to mobile phone usage patterns of the target population.

scholarly journals 0636 Using AI To Predict Future CPAP Adherence and the Impact of Behavioral and Technical Interventions

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A243-A243
Author(s):  
W Hevener ◽  
B Beine ◽  
J Woodruff ◽  
D Munafo ◽  
C Fernandez ◽  
...  
Keyword(s):  
Effect Size ◽  
Statistical Significance ◽  
Post Intervention ◽  
Cpap Adherence ◽  
Cohen’S D ◽  
Usage Patterns ◽  
Patient Outreach ◽  
Cohen's D ◽  
The Impact ◽  
Usage Data

Abstract Introduction Clinical management of CPAP adherence remains an ongoing challenge. Behavioral and technical interventions such as patient outreach, coaching, troubleshooting, and resupply may be deployed to positively impact adherence. Previous authors have described adherence phenotypes that retrospectively categorize patients by discrete usage patterns. We design an AI model that predictively categorizes patients into previously studied adherence phenotypes and analyzes the statistical significance and effect size of several types of interventions on subsequent CPAP adherence. Methods We collected a cross-sectional cohort of subjects (N = 13,917) with 455 days of daily CPAP usage data acquired. Patient outreach notes and resupply data were temporally synchronized with daily CPAP usage. Each 30-days of usage was categorized into one of four adherence phenotypes as defined by Aloia et al. (2008) including Good Users, Variable Users, Occasional Attempters, and Non-Users. Cross-validation was used to train and evaluate a Recurrent Neural Network model for predicting future adherence phenotypes based on the dynamics of prior usage patterns. Two-sided 95% bootstrap confidence intervals and Cohen’s d statistic were used to analyze the significance and effect size of changes in usage behavior 30-days before and after administration of several resupply interventions. Results The AI model predicted the next 30-day adherence phenotype with an average of 90% sensitivity, 96% specificity, 95% accuracy, and 0.83 Cohen’s Kappa. The AI model predicted the number of days of CPAP non-use, use under 4-hours, and use over 4-hours for the next 30-days with OLS Regression R-squared values of 0.94, 0.88, and 0.95 compared to ground truth. Ten resupply interventions were associated with statistically significant increases in adherence, and ranked by adherence effect size using Cohen’s d. The most impactful were new cushions or masks, with a mean post-intervention CPAP adherence increase of 7-14% observed in Variable User, Occasional Attempter, and Non-User groups. Conclusion The AI model applied past CPAP usage data to predict future adherence phenotypes and usage with high sensitivity and specificity. We identified resupply interventions that were associated with significant increases in adherence for struggling patients. This work demonstrates a novel application for AI to aid clinicians in maintaining CPAP adherence. Support  

Visualization based API usage patterns refining

2015 ◽  
Author(s):  
Mohamed Aymen Saied ◽  
Omar Benomar ◽  
Houari Sahraoui
Keyword(s):  
Usage Patterns ◽  
Api Usage

scholarly journals A Snapshot of Early Adopters of E-journals: Challenges to the Library

2002 ◽  
Vol 63 (6) ◽  
pp. 515-526 ◽  
Author(s):  
Martin J. Brennan ◽  
Julie M. Hurd ◽  
Deborah D. Blecic ◽  
Ann C. Weller
Keyword(s):  
Electronic Journals ◽  
Early Adopters ◽  
Research And Teaching ◽  
Library Resources ◽  
Usage Patterns ◽  
Changing Practices ◽  
Perceptions And Expectations ◽  
The University ◽  
Qualitative Exploration ◽  
The Impact

Studies documenting the usage patterns of electronic journals have compared print and e-journal characteristics, surveyed faculty for their perceptions and expectations, and analyzed the impact on library practices. This study, a qualitative exploration of a wide array of issues related to the research and teaching habits of early adopters of e-journals in a research setting, was conducted in the spring of 2001 with faculty in the basic and health sciences at the University of Illinois at Chicago. Open-ended questionnaires provided a framework to wide-ranging discussions of perceptions, expectations, and changing practices pertaining to e-journals and other electronic resources. The results were analyzed with a specific focus on shared behaviors and values, discipline-dependent variations, and changing research and teaching habits. Several challenges for library resources and services are identified and discussed.

Could We Infer Unordered API Usage Patterns Only Using the Library Source Code?

2015 ◽  
Author(s):  
Mohamed Aymen Saied ◽  
Hani Abdeen ◽  
Omar Benomar ◽  
Houari Sahraoui
Keyword(s):  
Source Code ◽  
Usage Patterns ◽  
Api Usage
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