Testing the Performance of Automated Documentation Generation with Included Inline Comments

A number of Machine Learning models utilize source code as training data for automating software development tasks. A common trend is to omit inline comments from source code in order to unify and standardize the examples, even though the additional information can capture important aspects and better explain algorithms. We claim that models,...

Comment Or Not To Comment: The Effects Of Comments On Method Name Prediction

Machine learning (ML) algorithms have been used frequently in the past years for Software Engineering tasks.<br/>One of the popular tasks researchers use is method name prediction, which helps them generate an identifier for methods with ML models such as Code2Seq.<br/>This model represents code snippets as Abstract Syntax Trees (AST) and...

Analysing the Impact of Inline Comments for the Task of Code Captioning

AI-assisted development tools use Machine Learning models to help developers achieve tasks such as Method Name Generation, Code Captioning, Smart Bug Finding and others. A common practice among data scientists training these models is to omit inline code comments from training data. We hypothesize that including inline comments in the training...

API evolution on Maven Central: do developers adhere to semantic versioning?

In this paper, we investigate whether developers of artifacts on Maven Central adhere to semantic versioning. We also investigate whether there is a link between violations in semantic versioning and the popularity of the violating method. Developers can violate semantic versioning by removing or altering methods in their API, which we refer to...

Method Popularity Distributions of Software Artefacts within Maven Central

Even though previous studies have studied software artefacts on a package level, little research has been done on a method level. In this work, we perform a method-level analysis to determine how popularity disperses among methods within software libraries of Maven Central. We analyse 384 software artefacts with three different metrics:...

An exploratory study about extent of use of released packages in the Maven Central Repository

Maven Central Repository hosts over 9 million repositories which ease software reuse. Since its appearance, Maven has been studied and character- ized using different popularity and quality metrics, in order to identify defining patterns and possible improvements. This study aims to analyze extent of use of packages released in the Maven Central...

Looking at changes in popularity in the Maven ecosystem

We look at the Maven eco-system and how popularity of packages and its methods change. We want to know if there are any trends that can help developers more efficiently use their time. To look at the popularity we do package analysis and method analysis. We find that there is no correlation between time and popularity on both the package and the...

FindItOut: a Crowdsourcing Discriminative Tacit Knowledge Elicitation Multiplayer Game using Pictures

Despite the ever-growing advances in artificial intelligence (AI), common sense acquisition and reasoning is still comparingly in their early stages to other fields in AI. To further advance this field, it is necessary to collect large amounts of common sense facts or tacit knowledge to train such AI models. One effective way is to use...

Designing a single player textual GWAP for validating tacit knowledge elicitation from crowds

Machine learning can still make harmful mistakes. A solution would be tacit knowledge. Machine learning needs this type of knowledge to improve. An example of such knowledge that can help make the system draw better logical conclusions would be: if presented with an open fridge, then it could deduct that the food will go bad. Tacit knowledge or...

Designing a Multiplayer Competitive Text-Based Game for Elicitation of Tacit Knowledge About Humor from Crowds of People

Tacit knowledge, unlike explicit knowledge, is not easily codifiable, yet important for machine learning models. This research explores a method to gather tacit knowledge about humor using a simple text-based party game, building on the existing idea of using games to gather tacit knowledge from crowds of people. Players propose prompts, which...

SceneFinder: A collaborative game that collects tacit knowledge from crowds

The manual process of collecting and labelling data required for machine learning tasks is labour-intensive, expensive, and time consuming. In the past, efforts have been made to crowdsource this data by either offering people monetary incentives, or by using a gamified approach where users contribute to databases as a side-effect of playing an...

Swipe for Science: a single-player game designed for collecting discriminative evidence

We introduce Swipe for Science, a single-player mobile game designed for collecting discriminative evidence from crowds. When people play this game, we collect data about how certain concepts are associated with different contexts, which is valuable knowledge for machine learning systems. The main gameplay loop consists of swiping concepts up,...

Analysing the Effect of Asymmetry on the Performance of Atomic Ensemble Based Repeater Protocols

There has been a lot of research focused on the next generation of the internet, the so-called quantum networks. This analysis has been so far limited to mostly symmetrical architectures, but any near-term realisations of quantum networks using existing fibre topologies will contain asymmetry. In this thesis, we investigate how midpoint...

Memory-Disaggregated In-Memory Object Store Framework for Big Data Applications

The concept of memory disaggregation has recently been gaining traction in research. With memory disaggregation, data center compute nodes are able to directly access memory on adjacent nodes and can therefore overcome local memory restrictions, introducing a new data management paradigm for distributed computing. This paper proposes and...