Measuring mashup similarity in open data innovation contests

Contests have become an important instrument for fostering the development of novel open data mash-ups, in short open data innovations. Literature calls for new methods for measuring the similarity of open data mash-ups in order to identify code cloning and creative re-use of components of applications. Theoretically grounded computationally methods for identifying the similarity of open data contests are lacking. This study explores the similarity measurement of data-based mashups in the context of an open data innovation contest. Three different dimensions of mashup similarity are defined: code similarity, functional feature similarity, and visualized feature similarity. The results from the contest, including the source code, the running project and the descriptive documents, are collected as the research data for this study. Data analysis is based on the design and development of computational approaches to measure technology and functional similarity. The findings of this study will be helpful in better understanding the similarity of solutions in an open data innovation contest. This study contributes to the theoretical and practical approaches for similarity measurement, especially in the field of mashup development

Voting in the European Union – Central Europe’s Lost Voice

Ten Central European countries became members of the European Union in the years 2004 - 2007. They constitute 20% of the EU’s total population; and even though their economic output is much lower, it rises dynamically. New members’ impact on the EU policies has nevertheless been limited. This is due not only to the arcane voting rules within the EU, but also to the lack of a common agenda among the Central European countries. Our paperillustrates that the new members rarely vote together and that their influence is thus fairly limited. We argue that as the EU seemingly lacks energy to implement further reforms that would stimulate its economy, impetus for change may come from Central European countries. To that end, however, they have to coordinate their voting and become a more coherent voting group than they are now.European Union, voting system, European Council, new member states

Learning based Methods for Code Runtime Complexity Prediction

Predicting the runtime complexity of a programming code is an arduous task. In fact, even for humans, it requires a subtle analysis and comprehensive knowledge of algorithms to predict time complexity with high fidelity, given any code. As per Turing's Halting problem proof, estimating code complexity is mathematically impossible. Nevertheless, an approximate solution to such a task can help developers to get real-time feedback for the efficiency of their code. In this work, we model this problem as a machine learning task and check its feasibility with thorough analysis. Due to the lack of any open source dataset for this task, we propose our own annotated dataset CoRCoD: Code Runtime Complexity Dataset, extracted from online judges. We establish baselines using two different approaches: feature engineering and code embeddings, to achieve state of the art results and compare their performances. Such solutions can be widely useful in potential applications like automatically grading coding assignments, IDE-integrated tools for static code analysis, and others.Comment: 14 pages, 2 figures, 8 table

Exploring Automated Code Evaluation Systems and Resources for Code Analysis: A Comprehensive Survey

The automated code evaluation system (AES) is mainly designed to reliably assess user-submitted code. Due to their extensive range of applications and the accumulation of valuable resources, AESs are becoming increasingly popular. Research on the application of AES and their real-world resource exploration for diverse coding tasks is still lacking. In this study, we conducted a comprehensive survey on AESs and their resources. This survey explores the application areas of AESs, available resources, and resource utilization for coding tasks. AESs are categorized into programming contests, programming learning and education, recruitment, online compilers, and additional modules, depending on their application. We explore the available datasets and other resources of these systems for research, analysis, and coding tasks. Moreover, we provide an overview of machine learning-driven coding tasks, such as bug detection, code review, comprehension, refactoring, search, representation, and repair. These tasks are performed using real-life datasets. In addition, we briefly discuss the Aizu Online Judge platform as a real example of an AES from the perspectives of system design (hardware and software), operation (competition and education), and research. This is due to the scalability of the AOJ platform (programming education, competitions, and practice), open internal features (hardware and software), attention from the research community, open source data (e.g., solution codes and submission documents), and transparency. We also analyze the overall performance of this system and the perceived challenges over the years

Understanding and Leveraging Crowd Development in Crowdsourcing

abstract: Although many examples have demonstrated the great potential of a human crowd as an alternative supplier in creative problem-solving, empirical evidence shows that the performance of a crowd varies greatly even under similar situations. This phenomenon is defined as the performance variation puzzle in crowdsourcing. Cases suggest that crowd development influences crowd performance, but little research in crowdsourcing literature has examined the issue of crowd development. This dissertation studies how crowd development impacts crowd performance in crowdsourcing. It first develops a double-funnel framework on crowd development. Based on structural thinking and four crowd development examples, this conceptual framework elaborates different steps of crowd development in crowdsourcing. By doing so, this dissertation partitions a crowd development process into two sub-processes that map out two empirical studies. The first study examines the relationships between elements of event design and crowd emergence and the mechanisms underlying these relationships. This study takes a strong inference approach and tests whether tournament theory is more applicable than diffusion theory in explaining the relationships between elements of event design and crowd emergence in crowdsourcing. Results show that that neither diffusion theory nor tournament theory fully explains these relationships. This dissertation proposes a contatition (i.e., contagious competition) perspective that incorporates both elements of these two theories to get a full understanding of crowd emergence in crowdsourcing. The second empirical study draws from innovation search literature and tournament theory to address the performance variation puzzle through analyzing crowd attributes. Results show that neither innovation search perspective nor tournament theory fully explains the relationships between crowd attributes and crowd performance. Based on the research findings, this dissertation discovers a competition-search mechanism beneath the variation of crowd performance in crowdsourcing. This dissertation makes a few significant contributions. It maps out an emergent process for the first time in supply chain literature, discovers the mechanisms underlying the performance implication of a crowd-development process, and answers a research call on crowd engagement and utilization. Managerial implications for crowd management are also discussed.Dissertation/ThesisDoctoral Dissertation Business Administration 201