Influence of developer factors on code quality: a data study

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Automatic source-code inspection tools help to assess, monitor and improve code quality. Since these tools only examine the software project’s codebase, they overlook other possible factors that may impact code quality and the assessment of the technical debt (TD). Our initial hypothesis is that human factors associated with the software developers, like coding expertise, communication skills, and experience in the project have some measurable impact on the code quality. In this exploratory study, we test this hypothesis on two large open source repositories, using TD as a code quality metric and the data that may be inferred from the version control systems. The preliminary results of our statistical analysis suggest that the level of participation of the developers and their experience in the project have a positive correlation with the amount of TD that they introduce. On the contrary, communication skills have barely any impact on TD.Peer ReviewedPostprint (author's final draft

A Neural Network Classifier for the COI Barcode Gene

Mitochondrial Cytochrome C Oxidase subunit I (CO I – to be read as “see – oh one”) is a 658 base pair region in the gene encoding that is proposed as standard barcode for animals. Meaning, the CO I is a special region found in animal DNA that is studied to identify the species of the animal. Currently, there is an implementation of an algorithm called ARBitrator which identifies and extracts these CO I sequences from enormous genes database called GenBank. The ARBitrator is good at extracting the CO I sequences that have better specificity and accuracy as compared to other existing algorithms for CO I sequence identification[1][2]. Now, this project aims at training a neural network to learn the features of the CO I sequences extracted by ARBitrator, so that this neural network can be used in future to further recognize CO I sequences. Effectively, we are aiming to successfully design, train, and use a deep learning neural network to learn to recognize CO I sequences in a supervised way. This is the first time that a neural network is explored and used for this purpose

International conference on software engineering and knowledge engineering: Session chair

The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing. The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome