Why Mining Execs Don’t Care if Congo Hikes Up its Profit Tax

African Argument

Modelling Data Mining Dynamic Code Attributes with Scheme Definition Technique

Data mining is a technique used in differentdisciplines to search for significant relationships among variablesin large data sets. One of the important steps on data mining isdata preparation. On these step, we need to transform complexdata with more than one attributes into representative format fordata mining algorithm. In this study, we concentrated on thedesigning a proposed system to fetch attributes from a complexdata such as product ID. Then the proposed system willdetermine the basic price of each product based on hiddenrelationships among the attributes of data. These researchesconclude that the proposed system accuracy of precision rate is98.7% and recall rate are 70.27%

Comparative analyses of the return on investment of 2013 and 2015 mineral policy reforms in Burkina Faso

This paper evaluates the effects of the changes in Burkina Faso’s mineral policies of 2013 and 2015 and their economic attractiveness using the Natougou Project as case study. Cash flow analyses shows a higher NPV of US $93,817,833.84 and an IRR of 44.67$ 321,488,366.61 will accrue to the government for 2015 Mining Code as against US$ 245,442,053.07 for the 2003 Mining Code.Keywords: Mining Code, investment, physical policies, dividends, sensitivity analysis, government and projec

git2net - Mining Time-Stamped Co-Editing Networks from Large git Repositories

Data from software repositories have become an important foundation for the empirical study of software engineering processes. A recurring theme in the repository mining literature is the inference of developer networks capturing e.g. collaboration, coordination, or communication from the commit history of projects. Most of the studied networks are based on the co-authorship of software artefacts defined at the level of files, modules, or packages. While this approach has led to insights into the social aspects of software development, it neglects detailed information on code changes and code ownership, e.g. which exact lines of code have been authored by which developers, that is contained in the commit log of software projects. Addressing this issue, we introduce git2net, a scalable python software that facilitates the extraction of fine-grained co-editing networks in large git repositories. It uses text mining techniques to analyse the detailed history of textual modifications within files. This information allows us to construct directed, weighted, and time-stamped networks, where a link signifies that one developer has edited a block of source code originally written by another developer. Our tool is applied in case studies of an Open Source and a commercial software project. We argue that it opens up a massive new source of high-resolution data on human collaboration patterns.Comment: MSR 2019, 12 pages, 10 figure

A Mining Algorithm for Extracting Decision Process Data Models

The paper introduces an algorithm that mines logs of user interaction with simulation software. It outputs a model that explicitly shows the data perspective of the decision process, namely the Decision Data Model (DDM). In the first part of the paper we focus on how the DDM is extracted by our mining algorithm. We introduce it as pseudo-code and, then, provide explanations and examples of how it actually works. In the second part of the paper, we use a series of small case studies to prove the robustness of the mining algorithm and how it deals with the most common patterns we found in real logs.Decision Process Data Model, Decision Process Mining, Decision Mining Algorithm

Declarative visitors to ease fine-grained source code mining with full history on billions of AST nodes

Software repositories contain a vast wealth of information about software development. Mining these repositories has proven useful for detecting patterns in software development, testing hypotheses for new software engineering approaches, etc. Specifically, mining source code has yielded significant insights into software development artifacts and processes. Unfortunately, mining source code at a large-scale remains a difficult task. Previous approaches had to either limit the scope of the projects studied, limit the scope of the mining task to be more coarse-grained, or sacrifice studying the history of the code due to both human and computational scalability issues. In this paper we address the substantial challenges of mining source code: a) at a very large scale; b) at a fine-grained level of detail; and c) with full history information. To address these challenges, we present domain-specific language features for source code mining. Our language features are inspired by object-oriented visitors and provide a default depth-first traversal strategy along with two expressions for defining custom traversals. We provide an implementation of these features in the Boa infrastructure for software repository mining and describe a code generation strategy into Java code. To show the usability of our domain-specific language features, we reproduced over 40 source code mining tasks from two large-scale previous studies in just 2 person-weeks. The resulting code for these tasks show between 2.0x--4.8x reduction in code size. Finally we perform a small controlled experiment to gain insights into how easily mining tasks written using our language features can be understood, with no prior training. We show a substantial number of tasks (77%) were understood by study participants, in about 3 minutes per task