On the need for open teaching on the JamStack

Typical university Learning Management Systems (LMSs) place an enrolment paywall between students and the content within a unit. This has the effect not only of preventing access from potential students, but also of locking past students out from accessing updated materials as the subject develops over subsequent years to their enrolment. In this and many regards, the mechanisms by which academics can produce and publish content face limitations that open source software documentation sites do not. This provocation paper describes some of these limitations and gives an overview of the JamStack – common techniques that have developed within the software development community that allow convenient self-publishing of sites and materials. The paper then gives a brief introduction to Doctacular: a course-oriented static site generator that is under development (but already used for two live sites) to bring JamStack-style publishing to academic course materials.

Software documentation is not enough! Requirements for the documentation of AI

Purpose Artificial intelligence (AI) is currently one of the most disruptive technologies and can be applied in many different use cases. However, applying AI in regulated environments is challenging, as it is currently not clear how to achieve and assess the fairness, accountability and transparency (FAT) of AI. Documentation is one promising governance mechanism to ensure that AI is FAT when it is applied in practice. However, due to the nature of AI, documentation standards from software engineering are not suitable to collect the required evidence. Even though FAT AI is called for by lawmakers, academics and practitioners, suitable guidelines on how to document AI are not available. This interview study aims to investigate the requirements for AI documentations. Design/methodology/approach A total of 16 interviews were conducted with senior employees from companies in the banking and IT industry as well as with consultants. The interviews were then analyzed using an informed-inductive coding approach. Findings The authors found five requirements for AI documentation, taking the specific nature of AI into account. The interviews show that documenting AI is not a purely technical task, but also requires engineers to present information on how the AI is understandably integrated into the business process. Originality/value This paper benefits from the unique insights of senior employees into the documentation of AI.

CytoPy: An autonomous cytometry analysis framework

Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is open source and available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.

Tailoring effective requirement's specification for ingenuity in Software Development Life Cycle.

Software Requirements Engineering (SRE) process define software manuscripts with sustaining Software Requirement Specification (SRS) and its activities. SRE comprises many tasks requirement analysis, elicitation, documentation, conciliation and validation. Natural language is most popular and commonly used to form the SRS document. However, natural language has its own limitations wrt quality approach for SRS. The constraints include incomplete, incorrect, ambiguous, and inconsistency. In software engineering, most applications are object-oriented. So requirements are unlike problem domain need to be developed. So software documentation is completed in such a way that, all authorized users like clients, analysts, managers, and developers can understand it. These are the basis for success of any planned project. Most of the work is still dependent on intensive human (domain expert) work. consequences of the project success still depend on timeliness with tending errors. The fundamental quality intended for each activity is specified during the software development process. This paper concludes critically with best practices in writing SRS. This approach helps to mitigate SRS limitation up to some extent. An initial review highlights capable results for the proposed practices

Automated approach to semantic search through software documentation based on Doc2Vec algorithm

Introduction: An important stage in a software development life cycle is the support phase, when customers can contact the support service of the supplier company and request a solution to an issue encountered in the software. To solve the request, engineers often have to refer to the relevant documentation. In order to reduce the complexity of the maintenance phase, the search for the necessary documentation pages can be automated. Purpose: Development of an approach to semantic search through documentation using Doc2Vec machine learning algorithm in order to automate the solution of customer requests. Results: An approach is proposed to semantic search through text documentation files and wiki pages using Doc2Vec machine learning algorithm. The documentation pages with semantic similarities to the textual description of an unresolved customer request help the engineer to process the request more efficiently and rapidly. Based on the proposed approach, a software tool has been developed which provides the engineer with a report containing links to documentation pages semantically related to the unresolved request. During the configuration of this tool, the optimal parameters of the Doc2Vec algorithm were found, providing the necessary quality of the semantic search. The idea of the experiment was to apply the tool to unresolved requests and evaluate its effectiveness. The developed approach and software tool were successfully tested in an open source Apache Kafka project. In the course of the experiment, 100 requests from Jira bug tracking system were downloaded and analyzed. The experimental results show the advantage of using the tool in software product support. The average documentation analysis time has been reduced as compared to the traditional manual approach. Practical relevance: The research results were used to solve real customer requests. The developed approach and the software implemented on its basis can reduce the complexity of the maintenance phase.

Case study: Employing Agile Tools in Teaching Product Development to Mechatronics Students

Agile tools such as Git are widely used in the industry for source control, collaboration and documentation. Such tools have been implemented in a mechatronic product development course to allow for easier collaboration between students. The course content is mainly provided using a GitLab Pages webpage which hosts software documentation and scripts. This course was first changed in 2019 to include the development of an autonomous strawberry picker. However, the use of standard learning management system and lecture slides provided a cumbersome experience for the students. Therefore, these agile tools were presented in 2020 version to improve the course. In this paper, the course content is detailed, and student feedback from both years are discussed to reveal the outcome of the changes.