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The APPS dataset consists of problems collected from different open-access coding websites such as Codeforces, Kattis, and more. The APPS benchmark attempts to mirror how humans programmers are evaluated by posing coding problems in unrestricted natural language and evaluating the correctness of solutions. The problems range in difficulty from introductory to collegiate competition level and measure coding ability as well as problem-solving. The Automated Programming Progress Standard, abbreviated APPS, consists of 10,000 coding problems in total, with 131,836 test cases for checking solutions and 232,444 ground-truth solutions written by humans. Problems can be complicated, as the average length of a problem is 293.2 words. The data are split evenly into training and test sets, with 5,000 problems each. In the test set, every problem has multiple test cases, and the average number of test cases is 21.2. Each test case is specifically designed for the corresponding problem, enabling us to rigorously evaluate program functionality. Source: Measuring Coding Challenge Competence With APPS Image source: Measuring Coding Challenge Competence With APPS

Extension test cases of APPS, as well as generated code.

The peer-reviewed paper of AWARE dataset is published in ASEW 2021, and can be accessed through: http://doi.org/10.1109/ASEW52652.2021.00049. Kindly cite this paper when using AWARE dataset. Aspect-Based Sentiment Analysis (ABSA) aims to identify the opinion (sentiment) with respect to a specific aspect. Since there is a lack of smartphone apps reviews dataset that is annotated to support the ABSA task, we present AWARE: ABSA Warehouse of Apps REviews. AWARE contains apps reviews from three different domains (Productivity, Social Networking, and Games), as each domain has its distinct functionalities and audience. Each sentence is annotated with three labels, as follows: Aspect Term: a term that exists in the sentence and describes an aspect of the app that is expressed by the sentiment. A term value of “N/A” means that the term is not explicitly mentioned in the sentence. Aspect Category: one of the pre-defined set of domain-specific categories that represent an aspect of the app (e.g., security, usability, etc.). Sentiment: positive or negative. Note: games domain does not contain aspect terms. We provide a comprehensive dataset of 11323 sentences from the three domains, where each sentence is additionally annotated with a Boolean value indicating whether the sentence expresses a positive/negative opinion. In addition, we provide three separate datasets, one for each domain, containing only sentences that express opinions. The file named “AWARE_metadata.csv” contains a description of the dataset’s columns. How AWARE can be used? We designed AWARE such that it can be used to serve various tasks. The tasks can be, but are not limited to: Sentiment Analysis. Aspect Term Extraction. Aspect Category Classification. Aspect Sentiment Analysis. Explicit/Implicit Aspect Term Classification. Opinion/Not-Opinion Classification. Furthermore, researchers can experiment with and investigate the effects of different domains on users' feedback. Source: AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation

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GLARE is an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews from 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the gathered reviews. We also highlight possible use cases and benefits of the dataset.

In this repository, we provide the set-up files and output files of 5 behavioral observation data entry applications. These applications allow observers to collect animal behavior data on a handheld computer (phone/tablet).

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