Machine Learning: A Software Process Reengineering in Software Development Organization (original) (raw)
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International Journal of Engineering and Advanced Technology, 2019
This research paper aims an analytical study on the software development organization insight into trending automation technologies and their implementation Software Engineering Management (SEM) processes. Software Project Management (SPM) is a scientific art for planning, controlling execution and monitoring. SPM approaches are more focusing towards the essential requirement for the success of software project development. It has been very challenging to manage software development using existing project management procedures driven by software development organizations and this is one of the areas of problem statement for this research. This paper discusses an analytical study for the requirements and consideration of BPR in SPM, explores to spot and emphasizes the important success factors for the execution of a BPR using benefits of Artificial Intelligence (AI) in software development organization. BPR is organizational mechanism that improves ability to respond to challenges of...
A Literature Review of Machine Learning and Software Development Life cycle Stages
IEEE Access, 2021
The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.
Understanding Development Process of Machine Learning Systems: Challenges and Solutions
Background: The number of Machine Learning (ML) systems developed in the industry is increasing rapidly. Since ML systems are different from traditional systems, these differences are clearly visible in different activities pertaining to ML systems software development process. These differences make the Software Engineering (SE) activities more challenging for ML systems because not only the behavior of the system is data dependent, but also the requirements are data dependent. In such scenario, how can Software Engineering better support the development of ML systems? Aim: Our objective is twofold. First, better understand the process that developers use to build ML systems. Second, identify the main challenges that developers face, proposing ways to overcome these challenges. Method: We conducted interviews with seven developers from three software small companies that develop ML systems. Based on the challenges uncovered, we proposed a set of checklists to support the developers. We assessed the checklists by using a focus group. Results: We found that the ML systems development follow a 4-stage process in these companies. These stages are: understanding the problem, data handling, model building, and model monitoring. The main challenges faced by the developers are: identifying the clients' business metrics, lack of a defined development process, and designing the database structure. We have identified in the focus group that our proposed checklists provided support during identification of the client's business metrics and in increasing visibility of the progress of the project tasks. Conclusions: Our research is an initial step towards supporting the development of ML systems, suggesting checklists that support developers in essential development tasks, and also serve as a basis for future research in the area.
The Nexus between the Machine Learning Techniques and Software Project Estimation
Global Disclosure of Economics and Business
Machine Learning is an application of artificial intelligence that allows computers to learn and develop without explicit programming. In other words, the goal of ML is to let computers learn on their own without human involvement and then alter their activities. ML also allows huge data processing. Project management planning and evaluation are vital in project execution. Project management is difficult without a realistic and logical plan. We give a complete overview of works on Machine Learning in Software Project Management. The first category contains software project management research articles. The third category includes research on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in production, and promotion of machine-learning project prediction. Our contribution also provides a broader viewpoint and context for future project risk management efforts. In conclusion...
The Challenges of Machine Learning in Software Development
Migration Letters, 2023
A documentary review was carried out on the production and publication of research papers related to the study of the variables Machine Learning and Software Development. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2017-2022 by Latin American institutions, achieving the identification of 307 publications. The information provided by this platform was organized through graphs and figures, categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors on the proposed topic is referenced through a qualitative analysis. Among the main findings made through this research, it is found that Brazil, with 153 publications, was the Latin American country with the highest scientific production registered in the name of authors affiliated with institutions of that nation. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material related to the study of Machine Learning and Software Development was Computer Science with 256 published documents, and the most used Publication Type during the period indicated above were Conference Articles with 52% of the total scientific production.
Software Project Management Using Machine Learning Technique—A Review
Applied Sciences
Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the r...
Towards an Intelligent Machine Learning-based Business Approach
International Journal of Intelligent Systems and Applications, 2022
With the constant increase of data induced by stakeholders throughout a product life cycle, companies tend to rely on project management tools for guidance. Business intelligence approaches that are project-oriented will help the team communicate better, plan their next steps, have an overview of the current project state and take concrete actions prior to the provided forecasts. The spread of agile working mindsets are making these tools even more useful. It sets a basic understanding of how the project should be running so that the implementation is easy to follow on and easy to use. In this paper, we offer a model that makes project management accessible from different software development tools and different data sources. Our model provide project data analysis to improve aspects: (i) collaboration which includes team communication, team dashboard. It also optimizes document sharing, deadlines and status updates. (ii) planning: allows the tasks described by the software to be us...
Machine Learning and Value Generation in Software Development: A Survey
Communications in Computer and Information Science
Machine Learning (ML) has become a ubiquitous tool for predicting and classifying data and has found application in several problem domains, including Software Development (SD). This paper reviews the literature between 2000 and 2019 on the use the learning models that have been employed for programming effort estimation, predicting risks and identifying and detecting defects. This work is meant to serve as a starting point for practitioners willing to add ML to their software development toolbox. It categorises recent literature and identifies trends and limitations. The survey shows as some authors have agreed that industrial applications of ML for SD have not been as popular as the reported results would suggest. The conducted investigation shows that, despite having promising findings for a variety of SD tasks, most of the studies yield vague results, in part due to the lack of comprehensive datasets in this problem domain. The paper ends with concluding remarks and suggestions for future research.
Implication of Artificial Intelligence in Software Development Life Cycle: A state of the art review
IJRRA, 2019
This paper will help the software industries to support the engineers and partners to have clear vision of situations and perspectives on client necessities. • This will stick the designers and clients all through the improvement cycle and this will build the certainty of clients. • Stakeholders and uniquely clients can get clear pictures of what sort of item these prerequisites will shape, so they can change at any stage. • This will concentrate more on individuals and correspondence against procedure and documentation.