Artificial Intelligence and Software Engineering: Are We Ready?

Mashkoor, Atif; Egyed, Alexander; Ramler, Rudolf

doi:10.1109/mc.2022.3144805

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…Many studies focused on software development education. Mashkoor et al [17] were happy that while SE is already helping AI prosper, the same is true in reverse, but at the same time, they are also frustrated about AI's "hit and miss" affair.…”

Section: Application Of Generative Aimentioning

confidence: 99%

A review of AI-assisted motion control

Li,

Chen,

Tsung

2023

International Conference on Internet of Things and Machine Learning (IoTML 2023)

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Section: Application Of Generative Aimentioning

confidence: 99%

A review of AI-assisted motion control

Li,

Chen,

Tsung

2023

International Conference on Internet of Things and Machine Learning (IoTML 2023)

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“…Umumnya, berbagai metoda yang ada masih terus dikaji dan ditelaah kinerjanya pada berbagai kasus yang tersedia. Metoda tersebut dapat dikategorikan ke dalam dunia kecerdasan buatan [9][10], terkadang masuk ke pembelajaran mesin [11]. Padahal satu pendekatan algoritma yang ada seringkali spesifik digunakan pada suatu kasus tertentu.…”

Section: Signifikansi Studiunclassified

Pendekatan Agile Scrum pada Pengembangan Aplikasi Analitik Akademik dan Kemahasiswaan

Rahutomo

Sutrisno

Sulistyo

et al. 2022

ISI

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Abstrack -To make the right decisions and policies, university management often requires data. The problem that often arises is scattered data in various existing applications. To get the right point of view, it requires data analysts who master the situation broadly, including the business processes that occur in the organization and the various applications that run in it. People who can handle the problem are very limited or even non-existent, even though the need for data from management is increasingly. It can overcome these problems by building a data analytics system that works in a data warehouse. So that this paper proposes to study the design and implementation of academic and student data analytics applications. Utilization of existing data warehouses can be used as data visualization, data reporting, trend analytics, association analytics, group analytics, decision support systems, forecasting systems, and expert systems. The existing approach uses the Agile Scrum framework to get around time constraints and limited people. The test results show that this application can be built with this framework. A sizable application can be built within 1 month with 1 scrum master, 4 programmers and 2 tester-documentator.

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“…Ensemble and soft computing approaches can address the issues encountered when employing algorithmic and expert judgement methods and are constantly on the rise to solve all critical problems (A. Mashkoor et al, 2022). However, ensemble use is too difficult due to issues related to the dependability of the achieved predictive accuracy score.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Approach for Effective Software Development Effort Estimation with Future Ranking

Eswara Rao,

Pydi,

Annan Naidu

et al. 2023

ADCAIJ

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To provide a client with a high-quality product, software development requires a significant amount of time and effort. Accurate estimates and on-time delivery are requirements for the software industry. The proper effort, resources, time, and schedule needed to complete a software project on a tight budget are estimated by software development effort estimation. To achieve high levels of accuracy and effectiveness while using fewer resources, project managers are improving their use of a model created to evaluate software development efforts properly as a decision-support system. As a result, this paper proposed that a novel model capable of determining precise accuracy of global and large-scale software products be developed with practical efforts. The primary goal of this paper is to develop and apply a practical ensemble approach for predicting software development effort. There are two parts to this study: the first phase uses machine learning models to extract the most useful features from previous studies. The development effort is calculated in the second phase using an advanced ensemble method based on the components of the first phase. The performance of the developed model outperformed the existing models after a controlled experiment was conducted to develop an ensemble model, evaluate it, and tune its parameters.

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Artificial Intelligence and Software Engineering: Are We Ready?

Cited by 13 publications

References 15 publications

A review of AI-assisted motion control

A review of AI-assisted motion control

Pendekatan Agile Scrum pada Pengembangan Aplikasi Analitik Akademik dan Kemahasiswaan

Ensemble Learning Approach for Effective Software Development Effort Estimation with Future Ranking

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