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 method level. We do seem to find that developers are slow to adopt newer versions. We also found that library maintainers tend to re-release older versions, these versions often have low usage.

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 Repository. Extent of use is a metric that quanti- fies how much of a package is used. The findings of this study show that on average 73.2% of avail- able methods and 77.1% modules in a package are used by dependents of the package. The study also shows that extent of use at method level is highly correlated with extent of use at module level (Pear- son correlation coefficient r = 0.829).

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: eigenvector centrality, degree centrality and dependent usage percentage. Using callgraphs of the interactions of a software artefact with its dependents, we can determine the relative popularity score of any method. We observe that popularity is inverse logarithmically distributed among the most frequently used methods within a library. Furthermore, 80% of calls to a library are to 26% of all methods, following the Pareto Principle. Likewise, the number of dependents per artefacts also follows a power-law distribution. We also find that no significant correlation exists between any of the analysed metrics, allowing opportunities for future research to determine a more accurate popularity metric. All of our results show that method popularity is logarithmically distributed within software artefacts of Maven Central.

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 as breaking changes. They can also violate semantic versioning by extending the API in a patch version, referred to as an illegal API extension. APIs that do not keep their promise of adhering to semantic versioning, will unexpectedly break their dependents during upgrading of dependencies.

We have found that these two types of violations do occur in practice. We find that 24% of analyzed artifacts contain breaking changes and 24% of artifacts contain illegal API extensions. Finally, we show that popularity of a method does not have an impact on breaking changes.

We conclude that semantic versioning can not always guarantee that upgrading dependencies will not lead to incompatibility. This indicates a need for developers to be more aware of the impact that violating semantic versioning has.

Machine learning (ML) algorithms have been used frequently in the past years for Software Engineering tasks.
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.
This model represents code snippets as Abstract Syntax Trees (AST) and includes all code elements except comments.
This paper introduces a novel approach to incorporating comments in Code2Seq.
The reimplemented models integrate comments into the AST when preprocessing the data.
We filtered comments to reduce the noise introduced in the model and improve the predictions using techniques such as TF-IDF and stopword removal.
We implement several models for each filtering technique and one for raw comments.
These models are trained and evaluated using the dataset java-small, provided by Code2Seq, with 700K examples and the following metrics: precision, recall, and F1.
The results obtained from the evaluation of our model with raw comments show an improvement of 3.62% and 2.36% in precision and F1, respectively, on method name prediction compared to the original model.
Furthermore, the model with stopword removal has a 6% and 3.52% gain in recall and F1. These improvements suggest that adding comments to Code2Seq is valuable for better method name predictions since they contain additional information about the methods.

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 code will provide more information to the model and improve the model's performance for natural-language related tasks, specifically Code Captioning. We adjust one of these models, code2seq, to include inline comments in its data processing, then train and compare it to a commentless version. We find that including inline comments tends to increase the performance of the model by making it faster and producing more verbose results, and then reflect on the results of this work to formulate suggestions on how to improve upon this body of research.

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 asymmetry affects quantum repeater protocols implemented with atomic ensembles. We extend the existing simulation framework to allow for midpoint asymmetry. By simulating asymmetry in elementary links, we show that the performance of an elementary link executing quantum key distribution decreases with an increasing degree of asymmetry. This effect can be mitigated by individual optimisation of photon sources at both ends of the elementary link. We present a way how to reduce the search space of such optimisations by developing a heuristic. The contributions of this thesis provide a crucial starting point for investigations of asymmetry in quantum repeater chains.

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 demonstrates a memory disaggregated in-memory object store framework for big data applications by leveraging the newly introduced ThymesisFlow memory disaggregation system. The framework extends the functionality of the existing Apache Arrow Plasma object store framework to distributed systems by enabling clients to easily and efficiently produce and consume data objects across multiple compute nodes. This allows big data applications to increasingly leverage parallel processing at reduced development cost. In addition, the paper includes latency and throughput measurements to evaluate the framework’s performance and to guide the design of future systems that leverage memory disaggregation as well as the newly presented framework.

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 common-sense knowledge refers to the type of knowledge which is acquired through experience, the kind only humans can create. GWAPs (game with a purpose) have shown quite promising results for acquiring such knowledge. Unfortunately, it could still contain errors due to users who only want to harm the game data, etc. and there is no method for validating such knowledge without involving humans somehow. Therefore, from the previously stated problem, our goal has emerged - develop a method for validating an existing data set and for later training machine learning models using a GWAP. There has been work done before using GWAPs to elicit such information, yet they are limited in the sense that their main focus is set on data collection, not validation. Since very few projects looked into it, we decided to investigate a new GWAP which has as main purpose tacit knowledge validation. The main question which we aim to answer is "How can we elicit and validate tacit knowledge using a game with the following settings: single-player, textual concepts, goal: associate words with their concepts." The game presents hints to the users and they have to guess, as fast as possible and with the least amount of tries as possible, which answer is correct from the 6 options that are provided. The evaluation of the game will be made using standard metrics such as games played, time spent playing, number of users, etc. The conclusion is that the GWAP, even with the lack of data, was quite capable of analyzing the quality of the data set and reached a conclusion that is easily confirmed by a mere look over the initial data set.

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 enjoyable game. However, most of these efforts focus on using a competitive setting to incentivize players. This sometimes results in users spamming the dataset for personal gains. Research is lacking in how a collaborative setup, where players work together to make decisions by consensus, can be used to source knowledge that is more accurate and reliable. This paper describes the design and evaluation of SceneFinder, a game that aims to crowdsource reliable and diverse textual data about scenes (such as rooms, parks, monuments, etc) and the tacit knowledge relevant to them, such as information about their contents, their purpose and their surroundings. SceneFinder makes use of a collaborative setup that elicits a relevance based ranking of facts about these scenes, that distinguishes it from existing games in the field.

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 crowdsourcing and games to make the collection process both widely available and fun at the same time. Currently, there are no tools to collect discriminative tacit knowledge efficiently and accurately. In this work, we propose FindItOut: a crowdsourcing tacit knowledge elicitation solution with multiplayer gamification and images to collect general-purpose and discriminative tacit knowledge. We show that using FindItOut, we can both efficiently and accurately collect discriminative tacit knowledge while also being fun and engaging.

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 will then be answered by other players. They will then vote to determine which of the two answers to each prompt is the funniest. The engagement of the players with the game is measured and tacit knowledge is obtained from the jokes. With a large and diverse enough group of participants across games, a variety of tacit knowledge can be extracted.

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, down, left, or right based on how these is associated with the given contexts. The game is simplistic and intuitive to control, and collects data of overall good quality, but it is not engaging enough to retain its players. Further development and research is required to unlock the full potential of this game.

ScenWise is an innovative company that specializes in data science revolving around traffic management. ScenWise strives to use the newest and best technologies and practices when it comes to web applications, data science and traffic management. The reason for this is that they provide tools to analyse and visualise a variety of situations that occur in traffic management. One such tool is SmartRoads 1.0, which allows users to analyse traffic data and situations via a web application. Unfortunately SmartRoads 1.0 does not perform as desired. Additionally, ScenWise itself has the problem of not being able to integrate previously made products by student groups into their own existing products. During the research aimed to resolve these problems another issue arose; the software development life cycle of ScenWise is very lacking. Research on the SmartRoads 1.0 performance problem showed that the bottleneck of its performance is due to the front-end. The outdated SmartRoads 1.0 front-end was thus replaced with a new and better SmartRoads 2.0 front-end. The integration problem and development life cycle problem are both addressed in the Longterm evolution (LTE) design found in appendix I. This LTE design contains the architecture migration plan. This plan will transform the current software architecture to a Service-oriented architecture (SOA) providing a solution for the current integration problems. A result of the first steps of this architecture migration plan is the Application Programming Interface (API) Gateway, which has been implemented in the aforementioned SmartRoads 2.0. Next to the migration plan, guidelines for ScenWise to improve their software development life cycle are elaborated in the LTE design. In this report the identified problems, their solutions and executions are explained, discussed and evaluated.

Recent times once more informed us on the relevance of capable online collaborative tools. For our online collaborative XML editor, we have looked into technologies for constrained block editing which, obeying schemas such as with XML, permit on- and offline users or agents to add, delete, copy, move, split and merge blocks of text. To that end, we studied the current state of Operational Transformations (OT) and Conflict-free Replicated Data Types (CRDT). Furthermore, after selection of the best-ranking enabling technology, we studied existing CRDT implementations for unstructured texts, and extended a Logoot-based CRDT to implement on-and offline split and merge block support. We designed a proof of concept and created a scientific prototype to deliver a proof of concept stability, reproducibility and convergence. For now, we excluded undo and redo operations. Given these conditions, we deliver emperical evidence of our implementation to converge under all circumstances, including split and acyclic block mergers. Finally, we give an outlook and design recommendations for production implementations, and suggestions for tackling the problem of cyclic references in block mergers.