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39 results for “assertion”
Uncovering the Semantics of Wikipedia Categories - Axioms and Assertions
<p>Resulting axioms and assertions from applying the Cat2Ax approach to the DBpedia knowledge graph.<br> The methodology is described in the conference publication "N. Heist, H. Paulheim: Uncovering the Semantics of Wikipedia Categories, International Semantic Web Conference, 2019".</p>
RAIDE: a tool for Assertion Roulette and Duplicate Assert identification and refactoring
<p>Video presentation of the article entitled "RAIDE: a tool for Assertion Roulette and Duplicate Assert identification and refactoring" of the 34th Brazilian Symposium on Software Engineering (SBES’20).</p>
Linguistic assertiveness
<p>A short animation about what is linguistic assertiveness.</p> <p>Credits - Concept art: Réka Kassay. Animation: Judith Vicsi, Bence Orosz. Graphics: Éva Páncél. Narration text: Erika Keszeg, Tibor Toró.</p>
Role-playing videos of linguistic assertiveness strategies
<p>These seven short videos show various linguistic assertiveness strategies in practice.</p> <ol> <li> <p>A ML speaker is greeting a group of people in a neutral way, not using the ML explicitly.</p> </li> </ol> <p>2. A ML speaker is greeting a group of people explicitly in the ML. By doing this, the other ML speakers can be identified much more easily.</p> <p>3. An example of pole position and of bilingual conversation. The first speaker starts the conversation in Welsh and although he gets an answer in English, her response tells him right away that the person he's talking to understands Welsh and that there is no reason to switch to English.</p> <p>4. If someone doesn’t understand you the first time, that doesn’t mean he or she doesn’t understand the ML. Just repeat what you were saying.</p> <p>5. An example of using pole position, repeating what you are saying and pointing. In very contextualised situations it usually gets you what you want without switching to the DL.</p> <p>6. Another example of using 1. pole position, 2. repeating what you are saying and 3. pointing.</p> <p>7. The strategies of pole position, bilingual conversation and how the ML speaker does not immediately switch to the DL, but instead translates the key words in the DL.</p>
A Survey of Assertions in Software Testing - Papers Classification
<p>The tabular listing of the classification of the collected papers for the literature survey of assertions in software testing.</p>
Linguistic Assertiveness
<p>A short animation - localized for Welsh speakers - about what is linguistic assertiveness.</p> <p>This animation is intended to be used as part of the Trainers' Toolkit of the LISTEN project (https://listen-europe.eu/trainers-toolkit/.</p> <p>*Full credits*</p> <p>Concept art: Reka Kassay, Sapientia University, Romania</p> <p>Animation: Judith Vicsi and Bence Orosz, Zoom Animation Studio, Romania</p> <p>Graphics: Éva Páncél, Zoom Animation Studio, Romania</p> <p>Narration text: Erika Keszeg, Bálványos Institute, Romania and Tibor Toró, Sapientia University, Romania</p> <p>Translation: IAITH (UK)</p> <p>Voice: Aled Wyn Hughes, Stiwdio Sain (UK)</p>
Linguistic Assertiveness
<p>Físeán beag maidir le treallús teangacha.</p> <p>*Full credits*</p> <p>Concept art: Réka Kassay, Sapientia University, Romania</p> <p>Animation: Vicsi Judith, Orosz Bence, Zoom Animation Studio, Romania</p> <p>Graphics: Éva Páncél, Zoom Animation Studio, Romania</p> <p>Narration text: Erika Keszeg, Bálványos Institute Romania, and Tibor Toró, Sapientia University, Romania</p> <p>Translation and Voice: Pádraig Ó Tiarnaigh, Conradh na Gaeilge, Ireland</p> <p>This material is intended to be used as part of the Trainers' Toolkit of the LISTEN project (https://listen-europe.eu/trainers-toolkit/).</p> <p> </p> <p> </p>
An Exploratory Study on the Usage and Readability of Messages Within Assertion Methods of Test Cases
<p>This is the code and dataset that accompanies the study: "<strong>An Exploratory Study on the Usage and Readability of Messages Within Assertion Methods of Test Cases</strong>." This study has been accepted for publication at the 2023 International Workshop on Natural Language-based Software Engineering.</p> <p><strong><em>Following is the abstract of the study:</em></strong></p> <p>Unit testing is a vital part of the software development process and involves developers writing code to verify or assert production code. Furthermore, to help comprehend the test case and troubleshoot issues, developers have the option to provide a message that explains the reason for the assertion failure. In this exploratory empirical study, we examine the characteristics of assertion messages contained in the test methods in 20 open-source Java systems. Our findings show that while developers rarely utilize the option of supplying a message, those who do, either compose it of only string literals, identifiers, or a combination of both types. Using standard English readability measuring techniques, we observe that a beginner's knowledge of English is required to understand messages containing only identifiers, while a 4th-grade education level is required to understand messages composed of string literals. We also discuss shortcomings with using such readability measuring techniques and common anti-patterns in assert message construction. We envision our results incorporated into code quality tools that appraise the understandability of assertion messages. </p>
Gassert: Evolutionary Improvement of Assertion Oracles - experimental data
<p>This dataset contains the data of our experiments that we conducted in our paper "Evolutionary Improvement of Assertion Oracles" published at ESEC/FSE 2020. <br> </p>
ATLAS+, Dataset for "An Empirical Study on Focal Methods in Deep-Learning-Based Approaches for Assertion Generation"
<p>Dataset for "An Empirical Study on Focal Methods in Deep-Learning-Based Approaches for Assertion Generation"</p>
On the Rationale and Use of Assertion Messages in Test Code: Insights from Software Practitioners
<p>This is the dataset for the paper: <strong>On the Rationale and Use of Assertion Messages in Test Code: Insights from Software Practitioners</strong></p> <p>This paper was accepted for publication at the International Conference on Software Maintenance and Evolution (ICSME 2024)</p> <p>Preprint: <a href="https://arxiv.org/abs/2408.01751" target="_blank" rel="noopener">https://arxiv.org/abs/2408.01751</a></p>
JMLKelinci+: Detecting Semantic Bugs and Covering Branches with Valid Inputs using Coverage-Guided Fuzzing and Runtime Assertion Checking
<p>Testing to detect semantic bugs is essential, especially for critical systems. Coverage-guided fuzzing and runtime assertion checking (RAC) are two well-known approaches for detecting semantic bugs. Coverage-guided fuzzing aims to generate inputs tests with high code coverage. However, while coverage-guided fuzzers are equipped with sanitizers that can detect a fixed set of semantic bugs, they can otherwise only detect bugs that lead to a crash. Thus, the first problem we address is how to help fuzzers detect previously unknown semantic bugs that do not lead to a crash. Moreover, a coverage-guided fuzzer may not necessarily cover all branches with valid inputs, although invalid inputs are useless for detecting semantic bugs. So, the second problem is how to guide a fuzzer to cover all branches in a program using only valid inputs. On the other hand, RAC monitors the expected behavior of a program dynamically and can only detect a semantic bug when a valid input test shows that the program does not satisfy its specification. <br> Thus, the third problem is how to provide high-quality input tests for a RAC that can trigger potential bugs.<br> The combination of a coverage-guided fuzzer and RAC solves these problems and can cover branches with valid inputs and detect semantic bugs effectively. Our study uses RAC to guarantee that only valid inputs reach the program under test using the program's specified preconditions and it also uses RAC to detect semantic bugs using specified postconditions. A prototype tool was developed for this study, named JMLKelinci+. Our results show that combining a coverage-guided fuzzer with RAC will lead to executing the program under test only with valid inputs and that this technique can effectively detect semantic bugs. <br> Also, this idea improves the feedback given to a coverage-guided fuzzer, enabling it to cover all branches faster in programs with non-trivial preconditions.</p>
Artifacts for paper "SAGA_Summarization-Guided Assert Statement Generation" submitted to JCST
<p>The project includes the source codes, datasets and experimental results used in the submitted JCST paper titled "SAGA: Summarization-Guided Assert Statement Generation"</p>
Refactoring Assertion Roulette and Duplicate Assert test smells: a controlled experiment [DATA]
<p>Data of the experiment with the RAIDE tool.</p>
Assertive Community Treatment (ACT) in Schizophrenia Spectrum Disorders
ClinicalTrials.gov study NCT01081418. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Assertive Community Treatment With CBT and SST for Schizophrenia
ClinicalTrials.gov study NCT02254733. IPD Sharing: NO. Countries: 1. Publications: 9.
Constructive Self Assertiveness Via the Internet
ClinicalTrials.gov study NCT04240249. IPD Sharing: YES. Countries: 1. Publications: 1.
A Stratified Sickle Event Randomized Trial (ASSERT)
ClinicalTrials.gov study NCT00102791. IPD Sharing: Not stated. Countries: 6. Publications: 1.
Evaluating a Digital Sexual Assertiveness Intervention for ASMM
ClinicalTrials.gov study NCT06878755. IPD Sharing: Not stated. Countries: 1. Publications: 1.
AID-trial Assertive Intervention After Deliberate Self-harm
ClinicalTrials.gov study NCT00700089. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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