InfoQ Homepage Applied Research Content on InfoQ
-
Measuring and Reducing the Environmental Impact of Software
Software applications often manage big amounts of data; most of them are internet-based applications, and incorporate artificial intelligence. According to Coral Calero, these three aspects improve the capabilities and functionalities provided by software but they have also increased the amount of energy needed. We need to measure energy consumption of software to control its environmental impact.
-
Experiences from Doing DORA Surveys Internally in Software Companies
Doing DORA surveys in your company can help you reflect on how you are doing software delivery and operation. The way you design and run the surveys, and how you analyze the results, largely impact the benefits that you can get out of them. Carlo Beschi shared his experiences from doing DORA surveys at Agile Cambridge.
-
How Continuous Discovery Helps Software Teams to Take Product Decisions
Continuous discovery for product development is regular research that involves the entire software product team, and that can actively inform product decisions. Equating continuous discovery to weekly conversations with one or more customers can be misleading. Combining quantitative and qualitative research methods can help software teams gather data and understand what is behind the data.
-
High-Performance Computing for Researchers and Students with Amazon Lightsail for Research
AWS recently announced the general availability (GA) of Amazon Lightsail for Research, a new offering designed to enable researchers and students to easily create and manage high-performance CPU or GPU research computers on the cloud.
-
The Challenges of Reading Code and How to Deal with Them
Reading code can be confusing in many ways; we are not explicitly taught how to read code, and we rarely practice code reading. Being aware of the cognitive processes that play a role can help to become better at reading code.
-
Lowering Recovery Time through AI-Enabled Troubleshooting
Machine learning algorithms for anomaly detection can assist DevOps in daily working routines, where generalized ML models are trained and applied to detect hidden patterns and identify suspicious behaviour. Applied machine learning for IT-operations (AIOPs) is starting to move from research environments to production environments in companies.
-
Stanford Research Center Studies Impacts of Popular Pretrained Models
Stanford University recently announced a new research center, the Center for Research on Foundation Models (CRFM), devoted to studying the effects of large pretrained deep networks (e.g. BERT, GPT-3, CLIP) in use by a surge of machine-learning research institutions and startups.
-
How Quantifying Information Leakage Helps to Protect Systems
Information leakage happens when observable information can be correlated with a secret. Secrets such as passwords, medical diagnosis, locations, or financial data uphold a lot of our world, and there are many types of information, like error messages or electrical consumption patterns, that can give hints to these secrets.
-
Artificial Intelligence for IT Operations: an Overview
Artificial intelligence for IT operations (AIOps) combines sophisticated methods from deep learning, data streaming processing, and domain knowledge to analyse infrastructure data from internal and external sources to automate operations and detect anomalies (unusual system behavior) before they impact the quality of service.
-
The Impact of Radical Uncertainty on People
Humans look for certainty as that makes them feel safe. Suddenly becoming an entirely distributed team due to the pandemic disrupted people. According to Kara Langford, radical uncertainty can cause people to believe they are in danger and lead to health issues. People will respond differently; uncertainty has also shown to lead to fresh ideas, innovations, and social good.
-
Simulating Agile Strategies with the Lazy Stopping Model
Simulation can be used to compare agile strategies and increase understanding of their strengths and weaknesses in different organisational and project contexts. The Lazy Stopping Model derived from the idea that we often fail to gather sufficient information to get an optimal result. Agile strategies can be simulated in the model as more or less effective defences against this “lazy stopping.”
-
Applying Cyberpsychology Research for a Positive Internet Experience
There is a lot of opinion and not enough fact on how we use the internet and the effect of the internet on our lives; the goal of cyberpsychology is to establish the facts, said Oonagh O'Brien. At RebelCon.io 2019 she spoke about her research on the use of the internet and its effects on student well-being and academic performance, and on positive use of and positive development on the internet.
-
Autonomous Vehicles Became Better at Predicting Lane-Changes
Researchers created an algorithm that allows self-driving cars to predict lane-changes of the surrounding cars. The system works by using a deep-learning technique called Long Short-Term Memories (LSTMs). Although the most likely scenario on the highway is that every car stays in its own lane, their algorithm was able to slightly improve on this baseline prediction.
-
How Personality Matters in Software Development
Leaders have to orchestrate diverse contributions from individuals with different personalities to build great teams. Team members might decide to act out of character and engage in behaviour outside their comfort zone to advance the team goal. To reduce the risk of burning out or compromising physical health, there should be restorative niches in which they can be their natural selves again.
-
Using Deep Learning Technologies IBM Reaches a New Milestone in Speech Recognition
The research team at IBM recently announced they've reached a new industry record at 5.5%, using the SWITCHBOARD linguistic corpus. This brings us closer to what's considered to be the human error rate, 5.1%. They used deep learning technologies and acoustic models to accomplish this milestone.