DyPyBench: A Benchmark of Executable Python Software

Python has emerged as one of the most popular programming languages, extensively utilized in domains such as machine learning, data analysis, and web applications. Python's dynamic nature and extensive usage make it an attractive candidate for dynamic program analysis. However, unlike for other popular languages, there currently is no comprehensive benchmark suite of executable Python projects, which hinders the development of dynamic analyses. This work addresses this gap by presenting DyPyBench, the first benchmark of Python projects that is large scale, diverse, ready to run (i.e., with fully configured and prepared test suites), and ready to analyze (by integrating with the DynaPyt dynamic analysis framework). The benchmark encompasses 50 popular opensource projects from various application domains, with a total of 681k lines of Python code, and 30k test cases. DyPyBench enables various applications in testing and dynamic analysis, of which we explore three in this work: (i) Gathering dynamic call graphs and empirically comparing them to statically computed call graphs, which exposes and quantifies limitations of existing call graph construction techniques for Python. (ii) Using DyPyBench to build a training data set for LExecutor, a neural model that learns to predict values that otherwise would be missing at runtime. (iii) Using dynamically gathered execution traces to mine API usage specifications, which establishes a baseline for future work on specification mining for Python. We envision DyPyBench to provide a basis for other dynamic analyses and for studying the runtime behavior of Python code

Lightweight, semi-automatic variability extraction: a case study on scientific computing

In scientific computing, researchers often use feature-rich software frameworks to simulate physical, chemical, and biological processes. Commonly, researchers follow a clone-and-own approach: Copying the code of an existing, similar simulation and adapting it to the new simulation scenario. In this process, a user has to select suitable artifacts (e.g., classes) from the given framework and replaces the existing artifacts from the cloned simulation. This manual process incurs substantial effort and cost as scientific frameworks are complex and provide large numbers of artifacts. To support researchers in this area, we propose a lightweight API-based analysis approach, called VORM, that recommends appropriate artifacts as possible alternatives for replacing given artifacts. Such alternative artifacts can speed up performance of the simulation or make it amenable to other use cases, without modifying the overall structure of the simulation. We evaluate the practicality of VORM—especially, as it is very lightweight but possibly imprecise—by means of a case study on the DUNE numerics framework and two simulations from the realm of physical simulations. Specifically, we compare the recommendations by VORM with recommendations by a domain expert (a developer of DUNE). VORM recommended 34 out of the 37 artifacts proposed by the expert. In addition, it recommended 2 artifacts that are applicable but have been missed by the expert and 32 artifacts not recommended by the expert, which however are still applicable in the simulation scenario with slight modifications. Diving deeper into the results, we identified an undiscovered bug and an inconsistency in DUNE, which corroborates the usefulness of VORM

Generative Artificial Intelligence and GPT using Deep Learning: A Comprehensive Vision, Applications Trends and Challenges

Generative Artificial intelligence is a prominent and recently emerging subdomain in the field of artificial intelligence. It deals with question-answering based on natural language processing. This paper discusses recent methodologies adopted by researchers in this field. It also discusses GAI and machine learning techniques for multimodal applications like image, text and audio-based data generation. This meta-analysis and survey was done from prominent research up to 2023 from the Scopus Database consisting of reputed and authenticated research papers.The research contribution is twofold 1. To analyze the recent research and applications at the industry level 2. To identify techniques and associated limitations. This would further aid practitioners  to address future challenges

Learning Analytics and Online Language learning

This chapter addresses the challenges and future potential of learning analytics. It examines some of the key questions raised by the research literature that will influence language education over the next decade, and investigates what kind of data can be used to inform effective decision-making in online language-learning contexts and how it can be visualized. The chapter turns to consider preliminary data arising from the needs analysis phase of the VITAL Project (Visualization Tools and Analytics to Monitor Online Language Learning and Teaching), a two-year EU-funded project that specifically addresses the gap in the research literature on analytics in language learning and teaching. Turning to the first large-scale project on learning analytics and online language learning, Link & Li's theoretical framework provides a useful starting point to consider the role of dashboards for language learners and instructors