Data Mining Practical Machine Learning Tools and Techniques

GeneCards, the human gene compendium, enables researchers to effectively navigate and inter-relate the wide universe of human genes, diseases, variants, proteins, cells, and biological pathways. Our recently launched Version 4 has a revamped infrastructure facilitating faster data updates, better-targeted data queries, and friendlier user experience. It also provides a stronger foundation for the GeneCards suite of companion databases and analysis tools. Improved data unification includes gene-disease links via MalaCards and merged biological pathways via PathCards, as well as drug information and proteome expression. VarElect, another suite member, is a phenotype prioritizer for next-generation sequencing, leveraging the GeneCards and MalaCards knowledgebase. It automatically infers direct and indirect scored associations between hundreds or even thousands of variant-containing genes and disease phenotype terms. VarElect's capabilities, either independently or within TGex, our comprehensive variant analysis pipeline, help prepare for the challenge of clinical projects that involve thousands of exome/genome NGS analyses. © 2016 by John Wiley & Sons, Inc.

The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses

This paper describes GenProg, an automated method for repairing defects in off-the-shelf, legacy programs without formal specifications, program annotations, or special coding practices. GenProg uses an extended form of genetic programming to evolve a program variant that retains required functionality but is not susceptible to a given defect, using existing test suites to encode both the defect and required functionality. Structural differencing algorithms and delta debugging reduce the difference between this variant and the original program to a minimal repair. We describe the algorithm and report experimental results of its success on 16 programs totaling 1.25 M lines of C code and 120K lines of module code, spanning eight classes of defects, in 357 seconds, on average. We analyze the generated repairs qualitatively and quantitatively to demonstrate that the process efficiently produces evolved programs that repair the defect, are not fragile input memorizations, and do not lead to serious degradation in functionality.

GenProg: A Generic Method for Automatic Software Repair

This survey provides a structured and comprehensive overview of research on security and privacy in computer and communication networks that use game-theoretic approaches. We present a selected set of works to highlight the application of game theory in addressing different forms of security and privacy problems in computer networks and mobile applications. We organize the presented works in six main categories: security of the physical and MAC layers, security of self-organizing networks, intrusion detection systems, anonymity and privacy, economics of network security, and cryptography. In each category, we identify security problems, players, and game models. We summarize the main results of selected works, such as equilibrium analysis and security mechanism designs. In addition, we provide a discussion on the advantages, drawbacks, and future direction of using game theory in this field. In this survey, our goal is to instill in the reader an enhanced understanding of different research approaches in applying game-theoretic methods to network security. This survey can also help researchers from various fields develop game-theoretic solutions to current and emerging security problems in computer networking.

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Game theory meets network security and privacy

Automatic program repair has been a longstanding goal in software engineering, yet debugging remains a largely manual process. We introduce a fully automated method for locating and repairing bugs in software. The approach works on off-the-shelf legacy applications and does not require formal specifications, program annotations or special coding practices. Once a program fault is discovered, an extended form of genetic programming is used to evolve program variants until one is found that both retains required functionality and also avoids the defect in question. Standard test cases are used to exercise the fault and to encode program requirements. After a successful repair has been discovered, it is minimized using structural differencing algorithms and delta debugging. We describe the proposed method and report experimental results demonstrating that it can successfully repair ten different C programs totaling 63,000 lines in under 200 seconds, on average.

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Automatically finding patches using genetic programming

In a large, long-lived project, an effective code review process is key to ensuring the long-term quality of the code base. In this work, we study code review practices of a large, open source project, and we investigate how the developers themselves perceive code review quality. We present a qualitative study that summarizes the results from a survey of 88 Mozilla core developers. The results provide developer insights into how they define review quality, what factors contribute to how they evaluate submitted code, and what challenges they face when performing review tasks. We found that the review quality is primarily associated with the thoroughness of the feedback, the reviewer's familiarity with the code, and the perceived quality of the code itself. Also, we found that while different factors are perceived to contribute to the review quality, reviewers often find it difficult to keep their technical skills up-to-date, manage personal priorities, and mitigate context switching.

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Code review quality: how developers see it

Code review is an essential element of any mature software development project; it aims at evaluating code contributions submitted by developers. In principle, code review should improve the quality of code changes (patches) before they are committed to the project's master repository. In practice, bugs are sometimes unwittingly introduced during this process. In this paper, we report on an empirical study investigating code review quality for Mozilla, a large open-source project. We explore the relationships between the reviewers' code inspections and a set of factors, both personal and social in nature, that might affect the quality of such inspections. We applied the SZZ algorithm to detect bug-inducing changes that were then linked to the code review information extracted from the issue tracking system. We found that 54% of the reviewed changes introduced bugs in the code. Our findings also showed that both personal metrics, such as reviewer workload and experience, and participation metrics, such as the number of involved developers, are associated with the quality of the code review process.

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Investigating code review quality: Do people and participation matter?

Organizations are generating, processing, and retaining data at a rate that often exceeds their ability to analyze it effectively; at the same time, the insights derived from these large data sets are often key to the success of the organizations, allowing them to better understand how to solve hard problems and thus gain competitive advantage. Because this data is so fast-moving and voluminous, it is increasingly impractical to analyze using traditional offline, read-only relational databases. Recently, new "big data" technologies and architectures, including Hadoop and NoSQL databases, have evolved to better support the needs of organizations analyzing such data. In particular, Elasticsearch - a distributed full-text search engine - explicitly addresses issues of scalability, big data search, and performance that relational databases were simply never designed to support. In this paper, we reflect upon our own experience with Elasticsearch and highlight its strengths and weaknesses for performing modern mining software repositories research.

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Mining modern repositories with elasticsearch

When submitting a patch, the primary concerns of individual developers are “How can I maximize the chances of my patch being approved, and minimize the time it takes for this to happen?” In principle, code review is a transparent process that aims to assess qualities of the patch by their technical merits and in a timely manner; however, in practice the execution of this process can be affected by a variety of factors, some of which are external to the technical content of the patch itself. In this paper, we describe an empirical study of the code review process for WebKit, a large, open source project; we replicate the impact of previously studied factors - such as patch size, priority, and component and extend these studies by investigating organizational (the company) and personal dimensions (reviewer load and activity, patch writer experience) on code review response time and outcome. Our approach uses a reverse engineered model of the patch submission process and extracts key information from the issue tracking and code review systems. Our findings suggest that these nontechnical factors can significantly impact code review outcomes.

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The influence of non-technical factors on code review

Assigning bug reports to individual developers is typically a manual, time-consuming, and tedious task. In this paper, we present a framework for automated assignment of bug-fixing tasks. Our approach employs preference elicitation to learn developer predilections in fixing bugs within a given system. This approach infers knowledge about a developer's expertise by analyzing the history of bugs previously resolved by the developer. We apply a vector space model to recommend experts for resolving bugs. When a new bug report arrives, the system automatically assigns it to the appropriate developer considering his or her expertise, current workload, and preferences. We address the task allocation problem by proposing a set of heuristics that support accurate assignment of bug reports to the developers.

/pdf/a-bug-you-like-a-framework-for-automated-assignment-of-bugs-241w43pe3f.pdf

Olga Baysal

Papers

Code review quality: how developers see it

Investigating code review quality: Do people and participation matter?

Mining modern repositories with elasticsearch

The influence of non-technical factors on code review

A bug you like: A framework for automated assignment of bugs