Related papers: Documenting Bioinformatics Software Via Reverse Engineering

Documenting Bioinformatics Software Via Reverse Engineering

URL: http://arxiv.org/abs/2305.04349v1
Date: Sun, 7 May 2023 18:12:05 GMT
Title: Documenting Bioinformatics Software Via Reverse Engineering
Authors: Vinicius Soares Silva Marques, Laurence Rodrigues do Amaral
Abstract summary: Documentation is one of the most neglected activities in Software Engineering. This paper highlights how one can document software that is already finished, using reverse engineering and thinking of the end-user.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Documentation is one of the most neglected activities in Software Engineering, although it is an important method of assuring quality and understanding. Bioinformatics software is generally written by researchers from fields other than Computer Science who usually do not provide documentation. Documenting bioinformatics software may ease its adoption in multidisciplinary teams and expand its impact on the community. In this paper, we highlight how one can document software that is already finished, using reverse engineering and thinking of the end-user.

Related papers

Estimating the Energy Footprint of Software Systems: a Primer [56.200335252600354]
quantifying the energy footprint of a software system is one of the most basic activities. This document aims to be a starting point for researchers who want to begin conducting work in this area.
arXiv Detail & Related papers (2024-07-16T11:21:30Z)
A Systematic Literature Review on the Use of Machine Learning in Software Engineering [0.0]
The study was carried out following the objective and the research questions to explore the current state of the art in applying machine learning techniques in software engineering processes. The review identifies the key areas within software engineering where ML has been applied, including software quality assurance, software maintenance, software comprehension, and software documentation.
arXiv Detail & Related papers (2024-06-19T23:04:27Z)
MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows [58.56005277371235]
We introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization of ScientificAspects. MASSW includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years. We demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset.
arXiv Detail & Related papers (2024-06-10T15:19:09Z)
Introducing High School Students to Version Control, Continuous Integration, and Quality Assurance [0.0]
Two high school students volunteered in our lab at Wayne State University where I'm a graduate research assistant and Ph.D. student in computer science. The students had taken AP Computer Science but had no prior experience with software engineering or software testing. This paper documents our experience devising a group project to teach the requisite software engineering skills to implement automated tests.
arXiv Detail & Related papers (2023-10-05T21:44:11Z)
Electronic Laboratory Notebook: A lazy approach [1.6350351668269407]
This paper presents an integrated solution for individuals and small groups of researchers in data-driven deductive research. Our electronic lab book generates itself out of notes and files, which are generated by one or several experiments. The generated electronic lab book is then presented on a Django-based website.
arXiv Detail & Related papers (2022-04-27T13:09:28Z)
On the validity of pre-trained transformers for natural language processing in the software engineering domain [78.32146765053318]
We compare BERT transformer models trained with software engineering data with transformers based on general domain data. Our results show that for tasks that require understanding of the software engineering context, pre-training with software engineering data is valuable.
arXiv Detail & Related papers (2021-09-10T08:46:31Z)
CitationIE: Leveraging the Citation Graph for Scientific Information Extraction [89.33938657493765]
We use the citation graph of referential links between citing and cited papers. We observe a sizable improvement in end-to-end information extraction over the state-of-the-art.
arXiv Detail & Related papers (2021-06-03T03:00:12Z)
A Survey of Deep Learning Approaches for OCR and Document Understanding [68.65995739708525]
We review different techniques for document understanding for documents written in English. We consolidate methodologies present in literature to act as a jumping-off point for researchers exploring this area.
arXiv Detail & Related papers (2020-11-27T03:05:59Z)
Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
arXiv Detail & Related papers (2020-05-27T11:56:56Z)
Documentation of Machine Learning Software [7.154621689269006]
Machine learning software documentation is different from most of the documentations that were studied in software engineering research. Our ultimate goal is automated generation and adaptation of machine learning software documents for users with different levels of expertise. We will investigate the Stack Overflow Q/As and classify the documentation related Q/As within the machine learning domain.
arXiv Detail & Related papers (2020-01-30T00:01:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.