Profiling Developers Through the Lens of Technical Debt

Context: Technical Debt needs to be managed to avoid disastrous consequences, and investigating developers' habits concerning technical debt management is invaluable information in software development. Objective: This study aims to characterize how developers manage technical debt based on the code smells they induce and the refactorings they apply. Method: We mined a publicly-available Technical Debt dataset for Git commit information, code smells, coding violations, and refactoring activities for each developer of a selected project. Results: By combining this information, we profile developers to recognize prolific coders, highlight activities that discriminate among developer roles (reviewer, lead, architect), and estimate coding maturity and technical debt tolerance

A Novel Approach for Learning How to Automatically Match Job Offers and Candidate Profiles

Automatic matching of job offers and job candidates is a major problem for a number of organizations and job applicants that if it were successfully addressed could have a positive impact in many countries around the world. In this context, it is widely accepted that semi-automatic matching algorithms between job and candidate profiles would provide a vital technology for making the recruitment processes faster, more accurate and transparent. In this work, we present our research towards achieving a realistic matching approach for satisfactorily addressing this challenge. This novel approach relies on a matching learning solution aiming to learn from past solved cases in order to accurately predict the results in new situations. An empirical study shows us that our approach is able to beat solutions with no learning capabilities by a wide margin.Comment: 15 pages, 6 figure

Low-Coders, No-Coders, and Citizen Developers in Demand: Examining Knowledge, Skills, and Abilities Through a Job Market Analysis

The emergence of low-code/no-code (LCNC) platform technologies and the resulting increase in citizen development programs are facilitating the democratization of the design, development, and deployment of digital solutions. Citizen developers, non-technical employees who leverage LCNC platforms, are at the heart of this trend. While many firms perceive LCNC and citizen development as a crucial component of their digital transformation strategy, little is known about the evolving roles in this field or the necessary knowledge, skills, and abilities (KSA). To address this knowledge gap, we processed 113,106 job postings published on Indeed.com. Our topic modeling methodology identified 34 KSA topics and classified them into the three domains platform, business, and technology. We contribute to research by empirically demonstrating which competencies are required to successfully work in the LCNC field. Our findings can guide individual professionals and organizations alike

Open data business models for media industry - Finnish case study

Governments and private companies have begun to make vast amounts of data resources available to the public without usage restrictions, in the form of open data. For example, Finnish governmental bureaus have made legal documents, statistics, geographical data, traffic data, and environmental data freely available for public use. These new data sources have enabled innovative services in several areas, and create a lucrative opportunity for media companies. Open data can enrich media content, for example, with live data streams, advanced visualizations, and context and location dependent information. This thesis identifies opportunities open data provides for media companies by conducting an extensive field study of the Finnish open data landscape. First, 15 companies pioneering in open data use are analysed to determine their offering, revenue model and resources, and the general value network in which they operate. These findings are then considered from the media company perspective in order to identify opportunities that open data provides for them. The open data industry in Finland is still in its early stages, but some commercial success can already be identified. This study grouped the examined companies into five profiles in an open data value network: (1) data analysers, (2) data extractors and transformers, (3) user experience providers, (4) commercial data publishers, and (5) support services and consultancy. These five profiles are grounded on both; the empirical findings of this study as well as the theoretical frameworks established by preceding academic papers. For media companies this research found three opportunity avenues; (1) use open data as a source in data journalism, (2) gather article ideas and content from the visual and numerical data analyses conducted by third-party analysers, or (3) achieve costs savings by publishing private data and using crowds to analyse it or creating user interfaces on top of it

Web Tracking: Mechanisms, Implications, and Defenses

This articles surveys the existing literature on the methods currently used by web services to track the user online as well as their purposes, implications, and possible user's defenses. A significant majority of reviewed articles and web resources are from years 2012-2014. Privacy seems to be the Achilles' heel of today's web. Web services make continuous efforts to obtain as much information as they can about the things we search, the sites we visit, the people with who we contact, and the products we buy. Tracking is usually performed for commercial purposes. We present 5 main groups of methods used for user tracking, which are based on sessions, client storage, client cache, fingerprinting, or yet other approaches. A special focus is placed on mechanisms that use web caches, operational caches, and fingerprinting, as they are usually very rich in terms of using various creative methodologies. We also show how the users can be identified on the web and associated with their real names, e-mail addresses, phone numbers, or even street addresses. We show why tracking is being used and its possible implications for the users (price discrimination, assessing financial credibility, determining insurance coverage, government surveillance, and identity theft). For each of the tracking methods, we present possible defenses. Apart from describing the methods and tools used for keeping the personal data away from being tracked, we also present several tools that were used for research purposes - their main goal is to discover how and by which entity the users are being tracked on their desktop computers or smartphones, provide this information to the users, and visualize it in an accessible and easy to follow way. Finally, we present the currently proposed future approaches to track the user and show that they can potentially pose significant threats to the users' privacy.Comment: 29 pages, 212 reference