Improving quality of software product line by analysing inconsistencies in feature models using an ontological rule‐based approach

Bhushan, Megha; Goel, Shivani; Kumar, Ajay

doi:10.1111/exsy.12256

Cited by 21 publications

(5 citation statements)

References 47 publications

(78 reference statements)

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“…The requirement filtering module is developed to filter out research projects that undergraduates do not meet the requirement by using rule‐based filtering techniques. Rule‐based approach uses a set of predefined rules to solve certain kinds of problems (Bhushan, Goel, & Kumar, ; Gao, Xu, & Wang, ). Similarly, rule‐based filtering techniques are the methods that use a set of rules to filter out irrelevant or unqualified items.…”

Section: The Proposed Methodsmentioning

confidence: 99%

A social recommendation system for academic collaboration in undergraduate research

Liu

Yang

et al. 2018

Expert Systems

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Academic collaboration plays an important role in undergraduate research. Current methods rely on offline social contacts for undergraduate students to collaborate with academic staff members in universities and research institutions. In big data era, it is difficult for undergraduate students to find suitable research project opportunities and supervisors to work with. This paper proposes a social recommendation system for undergraduate students to find research project opportunities and work with research project teams on an academic collaboration network. The proposed recommendation method integrates relevance, connectivity, and quality modules, where profiles of undergraduates are constructed with their self‐claimed information, research activities (e.g., studying and reading research publications and reading research projects), and social connections in the academic collaboration network. Suitable research projects are recommended based on the undergraduates' profiles. Experiments are conducted, and the results have shown that the proposed social recommendation system can facilitate undergraduates' selection of research projects.

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Section: The Proposed Methodsmentioning

confidence: 99%

A social recommendation system for academic collaboration in undergraduate research

Liu

Yang

et al. 2018

Expert Systems

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“…Exploiting commonality does not always result in cost reductions and customer satisfaction. [42][43][44] There are always market expectations regarding product differentiations based on customer needs. Therefore, management is a critical feature in SPLs; every reuse does not mean the existence of an SPL.…”

Section: Software Product Linementioning

confidence: 99%

“…Although the commonality seems to be at the core of the SPL approach, variations are as crucial as the commonalities from the point of marketing and customer. Exploiting commonality does not always result in cost reductions and customer satisfaction 42–44 . There are always market expectations regarding product differentiations based on customer needs.…”

Section: Introductionmentioning

confidence: 99%

A reference model for software product line capability assessment

Gökalp

Çalişkanbaş

Kocyigit

2023

J Software Evolu Process

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The Software Product Line (SPL) approach receives more attention due to the observed benefits, such as cost reduction, quality improvements, and reduced delivery time. Although organizations are aware of its potential benefits, they face some challenges while creating a clear road map to adopt this approach. Capability maturity models are developed to guide organizations by providing an extensive road map for improvement. Accordingly, we developed a capability maturity model, entitled SPL Capability Maturity Model (SPL‐CMM), to improve an organization's SPL capabilities by enabling the assessment of SPL‐specific processes and providing a guideline for process improvement. SPL‐CMM, developed based on SPICE‐ISO/IEC 330xx standard, has two dimensions of process and capability. The process dimension consists of 16 SPL‐specific processes defined under four process areas: business, architecture, technical, and organization, and the capability dimension has six capability and maturity levels from levels 0 to 5. It is a structured approach assisted by adequate measures with guidance on actions for improvement. A case study was conducted to check the applicability and usefulness of the proposed approach in a software company. The case study results show that SPL‐CMM is applicable for identifying the current state of the SPL process capability and the gaps for process improvement to the next level.

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“…Traditional ML models worked on structured datasets where the techniques were predefined for every step, the applied technique fails if any of the steps were missed. The process of evaluating the data quality used by ML and DL algorithms is essential [16][17][18][19][20][21][22]61]. Whereas, new algorithms adapt the omission of data based on the requirement for robustness of the algorithm.…”

Section: Machine Learningmentioning

confidence: 99%

Machine learning and deep learning approach for medical image analysis: diagnosis to detection

Rana

Bhushan

2022

Multimed Tools Appl

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Computer-aided detection using Deep Learning (DL) and Machine Learning (ML) shows tremendous growth in the medical field. Medical images are considered as the actual origin of appropriate information required for diagnosis of disease. Detection of disease at the initial stage, using various modalities, is one of the most important factors to decrease mortality rate occurring due to cancer and tumors. Modalities help radiologists and doctors to study the internal structure of the detected disease for retrieving the required features. ML has limitations with the present modalities due to large amounts of data, whereas DL works efficiently with any amount of data. Hence, DL is considered as the enhanced technique of ML where ML uses the learning techniques and DL acquires details on how machines should react around people. DL uses a multilayered neural network to get more information about the used datasets. This study aims to present a systematic literature review related to applications of ML and DL for the detection along with classification of multiple diseases. A detailed analysis of 40 primary studies acquired from the well-known journals and conferences between Jan 2014–2022 was done. It provides an overview of different approaches based on ML and DL for the detection along with the classification of multiple diseases, modalities for medical imaging, tools and techniques used for the evaluation, description of datasets. Further, experiments are performed using MRI dataset to provide a comparative analysis of ML classifiers and DL models. This study will assist the healthcare community by enabling medical practitioners and researchers to choose an appropriate diagnosis technique for a given disease with reduced time and high accuracy.

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Improving quality of software product line by analysing inconsistencies in feature models using an ontological rule‐based approach

Cited by 21 publications

References 47 publications

A social recommendation system for academic collaboration in undergraduate research

A social recommendation system for academic collaboration in undergraduate research

A reference model for software product line capability assessment

Machine learning and deep learning approach for medical image analysis: diagnosis to detection

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