Search for: All records

Infrastructure as code (IaC) scripts, such as Ansible scripts, are used to provision computing infrastructure at scale. Existence of bugs in IaC test scripts, such as, configuration and security bugs, can be consequential for the provisioned computing infrastructure. A characterization study of bugs in IaC test scripts is the first step to understand the quality concerns that arise during testing of IaC scripts, and also provide recommendations for practitioners on quality assurance. We conduct an empirical study with 4,831 Ansible test scripts mined from 104 open source software (OSS) repositories where we quantify bug frequency, and categorize bugs in test scripts. We further categorize testing patterns, i.e., recurring coding patterns in test scripts, which also correlate with appearance of bugs. From our empirical study, we observe 1.8% of 4,831 Ansible test scripts to include a bug, and 45.2% of the 104 repositories to contain at least one test script that includes bugs. We identify 7 categories of bugs, which includes security bugs and performance bugs that are related with metadata extraction. We also identify 3 testing patterns that correlate with appearance of bugs: 'assertion roulette’, 'local only testing’, and 'remote mystery guest‘. Based on our findings, we advocate for detection and mitigation of the 3 testing patterns as these patterns can have negative implications for troubleshooting failures, reproducible deployments of software, and provisioning of computing infrastructure. 

Abstract Lithium‐ion batteries (LIBs) have significantly impacted the daily lives, finding broad applications in various industries such as consumer electronics, electric vehicles, medical devices, aerospace, and power tools. However, they still face issues (i.e., safety due to dendrite propagation, manufacturing cost, random porosities, and basic & planar geometries) that hinder their widespread applications as the demand for LIBs rapidly increases in all sectors due to their high energy and power density values compared to other batteries. Additive manufacturing (AM) is a promising technique for creating precise and programmable structures in energy storage devices. This review first summarizes light, filament, powder, and jetting‐based 3D printing methods with the status on current trends and limitations for each AM technology. The paper also delves into 3D printing‐enabled electrodes (both anodes and cathodes) and solid‐state electrolytes for LIBs, emphasizing the current state‐of‐the‐art materials, manufacturing methods, and properties/performance. Additionally, the current challenges in the AM for electrochemical energy storage (EES) applications, including limited materials, low processing precision, codesign/comanufacturing concepts for complete battery printing, machine learning (ML)/artificial intelligence (AI) for processing optimization and data analysis, environmental risks, and the potential of 4D printing in advanced battery applications, are also presented. 

Machine learning (ML) operations or MLOps advocates for integration of DevOps- related practices into the ML development and deployment process. Adoption of MLOps can be hampered due to a lack of knowledge related to how development tasks can be automated. A characterization of bot usage in ML projects can help practitioners on the types of tasks that can be automated with bots, and apply that knowledge into their ML development and deployment process. To that end, we conduct a preliminary empirical study with 135 issues reported mined from 3 libraries related to deep learning: Keras, PyTorch, and Tensorflow. From our empirical study we observe 9 categories of tasks that are automated with bots. We conclude our work-in-progress paper by providing a list of lessons that we learned from our empirical study. 

The separation of oil from water and filtration of aqueous solutions and dispersions are critical issues in the processing of waste and contaminated water treatment. Membrane-based technology has been proven as an effective method for the separation of oil from water. In this research, novel vertical nanopores membrane, via oriented cylindrical block copolymer (BCP) films, suitable for oil/water filtration has been designed, fabricated and tested. We used a ∼100 nm thick model poly(styrene- block -methymethacrylate) (PS- b -PMMA) BCP as the active top nanofiltration layer, processed using a roll-to-roll (R2R) method of cold zone annealing (CZA) to obtain vertical orientation, followed by ultraviolet (UV) irradiation selective etch of PMMA cylinders to form vertically oriented nanopores as a novel feature compared to meandering nanopores in other reported BCP systems. The cylindrical nanochannels are hydrophilic, and have a uniform pore size (∼23 nm), a narrow pore size distribution and a high nanopore density (∼420 per sq. micron). The bottom supporting layer is a conventional microporous polyethersulfone (PES) membrane. The created asymmetric membrane is demonstrated to be effective for oil/water extraction with a modestly high throughput rate comparable to other RO/NF membranes. The molecular weight dependent filtration of a water soluble polymer, PEO, demonstrates the broader applications of such membranes.