How does Machine Learning Change Software Development Practices?

Wan, Zhiyuan; Xia, Xin; Lo, David; Murphy, Gail C.

doi:10.1109/tse.2019.2937083

Cited by 111 publications

(103 citation statements)

References 23 publications

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“…In 2015, Hanchate and Bichkar proposed [41] that Machine Learning (ML) based techniques like classification, case based reasoning, neutral networks, regression trees can improve prediction about the Software Project Management activities like requirements elicitation, planning and scheduling etc. Wan et al [42] performed qualitative and quantitative survey in 2019 and elicited significant difference between ML based system and non-ML based system development in Software Engineering aspects like planning, designing, testing with operational characteristics like differences in skills, problem solving techniques and task reusability, where they found ML based system development is more accurate and robust.…”

Section: Empirical Discussionmentioning

confidence: 99%

Machine Learning: A Software Process Reengineering in Software Development Organization

Bhavsar¹

2019

IJEAT

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BPR (Business Process Re-engineering) is an organizational mechanism that improves the organizational ability in responding to the challenges of qualitative result by change management and improvement in software engineering processes, productivity, product quality and competitive advantage. BPR inherits, explores and implements the building of process change, to incorporate enhancements to the essential considerations and protocols of (SEM) Software Engineering Management. Machine Learning (ML) can be the key aspect for BPR in software development organization. The goal of this research study is raising the conceptual vision about integration of automation technology like ML and its life cycle development within Software Development Life Cycle (SDLC) of the software product and highlights benefits and drawbacks ML techniques in SPM (Software Project Management), and how to implement ML in standard SEM practices. We have attempted the introduction of machine learning in SEM to determine specific performance and tasks reuse using empirical analysis and discussion on implementation of ML algorithms. The empirical study of software technologies includes control structure of an autonomous software application. In current era, ML imparts consistently promising accuracy in some SEM fields. The goal of this paper is an empirical and analytical study and literature review to propose desired level of quality software, through the comparative evaluation of existing processes and their respective support for Software Quality Engineering (SQE).

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Section: Empirical Discussionmentioning

confidence: 99%

Machine Learning: A Software Process Reengineering in Software Development Organization

Bhavsar¹

2019

IJEAT

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“…Interestingly, machine learning is not only benefiting from software engineering methods, but it changes software engineering at the same time. A recent study [110] presents the results of a large survey of software engineers on their experiences when developing ML-based products. The ability of a system to learn adds a layer of complexity and instability to the overall system design.…”

Section: Methodologies For ML Processesmentioning

confidence: 99%

Return on Investment in Machine Learning: Crossing the Chasm between Academia and Business

Mizgajski

Szymczak

Morzy

et al. 2020

Foundations of Computing and Decision Sciences

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Academia remains the central place of machine learning education. While academic culture is the predominant factor influencing the way we teach machine learning to students, many practitioners question this culture, claiming the lack of alignment between academic and business environments. Drawing on professional experiences from both sides of the chasm, we describe the main points of contention, in the hope that it will help better align academic syllabi with the expectations towards future machine learning practitioners. We also provide recommendations for teaching of the applied aspects of machine learning.

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“…One of the obtained ML models that exhibits a satisfactory performance is deployed (machine learning: model deployment). The performance of the deployed model should be monitored for a possible performance degradation [70,71]. When the performance does not meet expectations, a new ML model should be trained and deployed.…”

Section: Validationmentioning

confidence: 99%

Design of a Data Management Reference Architecture for Sustainable Agriculture

Giray¹,

Çatal

2021

Sustainability

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Effective and efficient data management is crucial for smart farming and precision agriculture. To realize operational efficiency, full automation, and high productivity in agricultural systems, different kinds of data are collected from operational systems using different sensors, stored in different systems, and processed using advanced techniques, such as machine learning and deep learning. Due to the complexity of data management operations, a data management reference architecture is required. While there are different initiatives to design data management reference architectures, a data management reference architecture for sustainable agriculture is missing. In this study, we follow domain scoping, domain modeling, and reference architecture design stages to design the reference architecture for sustainable agriculture. Four case studies were performed to demonstrate the applicability of the reference architecture. This study shows that the proposed data management reference architecture is practical and effective for sustainable agriculture.

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How does Machine Learning Change Software Development Practices?

Cited by 111 publications

References 23 publications

Machine Learning: A Software Process Reengineering in Software Development Organization

Machine Learning: A Software Process Reengineering in Software Development Organization

Return on Investment in Machine Learning: Crossing the Chasm between Academia and Business

Design of a Data Management Reference Architecture for Sustainable Agriculture

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