The potential of an adaptive computerized dynamic assessment tutor in diagnosing and assessing learners’ listening comprehension

In today’s environment of growing class sizes due to the prevalence of online and e-learning systems, providing one-to-one instruction and feedback has become a challenging task for teachers. Anyhow, the dialectical integration of instruction and assessment into a seamless and dynamic activity can provide a continuous flow of assessment information for teachers to boost and individualize learning. In this regard, adaptive learning technology is one way to facilitate teacher-supported learning and personalize curriculum and learning experiences. This study aimed to investigate the potential of an adaptive Computerized Dynamic Assessment (C-DA) tool applicable as a language diagnostician and assistant. The study tried to get insight into 75 Iranian EFL learners’ listening development by focusing on the learning potential exhibited through learners’ assessment and the degree of internalization of mediation. To achieve these, a C-DA tutor including two dynamic listening comprehension tests, each comprising 20 items, arranged in the order of difficulty was developed. The test takers unable to answer an item correctly were provided with graduated hints for different comprehension- and production-type items and the overall difficulty level of the test was adapted to the test takers’ proficiency level. In order to have a full diagnosis of each individual’s listening development, the adaptive C-DA automatically generated five test scores on each learner’s performance: actual (unmediated) score, mediated score, gain score, Learning Potential Score (LPS), and transfer score. The results of paired-sample t-tests revealed a significant development from the actual to the mediated scores. Furthermore, the LPSs indicated that the tutor was capable of revealing learners’ potential for learning. Moreover, learners with high LPS gained a higher mean for transfer scores followed by transfer scores of medium and low levels. The results of Mann-Whitney tests revealed a significant difference in the degree of internalization of mediation of learners with mid and low range of LPSs on the easy test and high and low range of LPSs on the difficult test. The findings of this research can have important theoretical and practical implications for researchers and educationalists. The instructional value of this adaptive C-DA tool lies in its unique opportunities for individualizing learning and developing individual learning plans in accordance with learners’ needs.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software Engineerin

CodeFill: Multi-token Code Completion by Jointly learning from Structure and Naming Sequences

Code completion is an essential feature of IDEs, yet current auto-completers are restricted to either grammar-based or NLP-based single token completions. Both approaches have significant draw-backs: grammar-based autocompletion is restricted in dynamically-typed language environments, whereas NLP-based autocompleters struggle to understand the semantics of the programming language and the developer's code context. In this work, we present CodeFill, a language model for autocompletion that combines learned structure and naming information. Using a parallel Transformer architecture and multi-task learning, CodeFill consumes sequences of source code token names and their equivalent AST token types. Uniquely, CodeFill is trained both for single-token and multi-token (statement) prediction, which enables it to learn long-range dependencies among grammatical and naming elements. We train CodeFill on two datasets, consisting of 29M and 425M lines of code, respectively. To make the evaluation more realistic, we develop a method to automatically infer points in the source code at which completion matters. We compare CodeFill against four baselines and two state-of-the-art models, GPT-C and TravTrans+. CodeFill surpasses all baselines in single token prediction (MRR: 70.9% vs. 66.2% and 67.8%) and outperforms the state of the art for multi-token prediction (ROUGE-L: 63.7% vs. 52.4% and 59.2%, for n=4 tokens). We publicly release our source code and datasets.Software EngineeringSoftware Technolog

Semantically-enhanced topic recommendation systems for software projects

Software-related platforms such as GitHub and Stack Overflow, have enabled their users to collaboratively label software entities with a form of metadata called topics. Tagging software repositories with relevant topics can be exploited for facilitating various downstream tasks. For instance, a correct and complete set of topics assigned to a repository can increase its visibility. Consequently, this improves the outcome of tasks such as browsing, searching, navigation, and organization of repositories. Unfortunately, assigned topics are usually highly noisy, and some repositories do not have well-assigned topics. Thus, there have been efforts on recommending topics for software projects, however, the semantic relationships among these topics have not been exploited so far. In this work, we propose two recommender models for tagging software projects that incorporate the semantic relationship among topics. Our approach has two main phases; (1) we first take a collaborative approach to curate a dataset of quality topics specifically for the domain of software engineering and development. We also enrich this data with the semantic relationships among these topics and encapsulate them in a knowledge graph we call SED-KGraph. Then, (2) we build two recommender systems; The first one operates only based on the list of original topics assigned to a repository and the relationships specified in our knowledge graph. The second predictive model, however, assumes there are no topics available for a repository, hence it proceeds to predict the relevant topics based on both textual information of a software project (such as its README file), and SED-KGraph. We built SED-KGraph in a crowd-sourced project with 170 contributors from both academia and industry. Through their contributions, we constructed SED-KGraph with 2,234 carefully evaluated relationships among 863 community-curated topics. Regarding the recommenders’ performance, the experiment results indicate that our solutions outperform baselines that neglect the semantic relationships among topics by at least 25% and 23% in terms of Average Success Rate and Mean Average Precision metrics, respectively. We share SED-KGraph, as a rich form of knowledge for the community to re-use and build upon. We also release the source code of our two recommender models, KGRec and KGRec+ (https://github.com/mahtab-nejati/KGRec).Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software Engineerin

Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Study

Transformer-based pre-trained models have recently achieved great results in solving many software engineering tasks including automatic code completion which is a staple in a developer’s toolkit. While many have striven to improve the code-understanding abilities of such models, the opposite – making the code easier to understand – has not been properly investigated. In this study, we aim to answer whether making code easier to understand through using contextual data improves the performance of pre-trained code language models for the task of code completion. We consider type annotations and comments as two common forms of additional contextual information that often help developers understand code better. For the experiments, we study code completion in two granularity levels; token and line completion and take three recent and large-scale language models for source code: UniXcoder, CodeGPT, and InCoder with five evaluation metrics. Finally, we perform the Wilcoxon Signed Rank test to gauge significance and measure the effect size. Contrary to our expectations, all models perform better if type annotations are removed (albeit the effect sizes are small). For comments, we find that the models perform better in the presence of multi-line comments (again with small effect sizes). Based on our observations, we recommend making proper design choices when training, fine-tuning, or simply selecting such models given the intended data and application. Better evaluations and multimodal techniques can also be further investigated to improve the practicality and accuracy of auto-completions.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software EngineeringSoftware Technolog

Targeted Attack on GPT-Neo for the SATML Language Model Data Extraction Challenge [PRESENTATION]

Previous work has shown that Large Language Models are susceptible to so-called data extraction attacks. This allows an attacker to extract a sample that was contained in the training data, which has massive privacy implications. The construction of data extraction attacks is challenging, current attacks are quite inefficient, and there exists a significant gap in the extraction capabilities of untargeted attacks and memorization. Thus, targeted attacks are proposed, which identify if a given sample from the training data, is extractable from a model. In this work, we apply a targeted data extraction attack to the SATML2023 Language Model Training Data Extraction Challenge. We apply a two-step approach. In the first step, we maximise the recall of the model and are able to extract the suffix for 69% of the samples. In the second step, we use a classifier-based Membership Inference Attack on the generations. Our AutoSklearn classifier achieves a precision of 0.841. The full approach reaches a score of 0.405 recall at a 10% false positive rate, which is an improvement of 34% over the baseline of 0.301.Software EngineeringSoftware Technolog

STACC: Code Comment Classification using SentenceTransformers

Code comments are a key resource for information about software artefacts. Depending on the use case, only some types of comments are useful. Thus, automatic approaches to clas-sify these comments have been proposed. In this work, we address this need by proposing, STACC, a set of SentenceTransformers- based binary classifiers. These lightweight classifiers are trained and tested on the NLBSE Code Comment Classification tool competition dataset, and surpass the baseline by a significant margin, achieving an average Fl score of 0.74 against the baseline of 0.31, which is an improvement of 139%. A replication package, as well as the models themselves, are publicly available.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software EngineeringSoftware Technolog

An implicit rheological model for numerical simulation of generalized Newtonian fluids

Fitting an explicit curve over some discrete data extracted from a rheometer is the usual way of writing a rheological model for generalized Newtonian fluids. These explicit models may not match totally with the extracted data and may ignore some features of the rheological behavior of the fluids. In this paper, a cubicspline curve fitting is used to fit a smooth curve from discrete rheological data. Spline interpolation avoids the problem of Runge's phenomenon, which occurs in interpolating using high degree polynomials. The formulation for applying presented rheological model is described in the context of least squares meshfree technique. One problem is solved to show validity of the scheme: a fluid with rather complex rheology model is considered and solved by both conventional explicit and proposed implicit models to show the advantages of the presented method.Process and EnergyElectrical Engineering, Mathematics and Computer Scienc

The NLBSE'23 Tool Competition

We report on the organization and results of the second edition of the tool competition from the International Workshop on Natural Language-based Software Engineering (NLBSE'23). As in the prior edition, we organized the competition on automated issue report classification, with a larger dataset. This year, we featured an extra competition on au-tomated code comment classification. In this tool competition edition, five teams submitted multiple classification models to automatically classify issue reports and code comments. The submitted models were fine-tuned and evaluated on a benchmark dataset of 1.4 million issue reports or 6.7 thousand code comments, respectively. The goal of the competition was to improve the classification performance of the baseline models that we provided. This paper reports details of the competition, including the rules, the teams and contestant models, and the ranking of models based on their average classification performance across issue report and code comment types.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software Engineerin