A methodology to rank the design patterns on the base of text relevancy

Hussain, Shahid; Keung, Jacky; Sohail, Mohammad; Khan, Arif Ali; Ilahi, Manzoor; Ahmad, Ghufran; Mufti, Muhammad Rafiq; Noor, Muhammad Asim

doi:10.1007/s00500-019-03882-y

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Haas and Hummel [83] applied learningto-rank to derive a scoring function that suggests extract method refactoring of long Java methods. Hussain et al [84] used learning-to-rank to create a prototype of an automated recommendation system to classify and select design patterns. Different from these works, our work is the first one to apply learning to rank to suggest file-level migrations.…”

Section: Related Workmentioning

confidence: 99%

An experience-based recommendation system to support migrations of Android applications from Java to Kotlin

Mateus,

Martinez,

Kolski

2021

Preprint

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In 2017, Google announced Kotlin as an official Android programming language, and more recently, as the preferred programming language to build applications. These facts motivated developers to migrate their applications, which is challenging because each migrated piece of code must be tested after the migration to ensure it preserves the expected behavior. Due to the interoperability between Java and Kotlin, most developers decided to migrate their applications gradually. Thus, developers have to decide which file(s) to migrate first on each migration step. However, there are no tools available to help developers make these choices. This paper presents an approach to support a gradual migration of Android applications that given a version of an application written in Java and eventually, in Kotlin, it suggests the most convenient files to migrate. To this end, we built a large-scale corpus of open-source projects that migrated Java files to Kotlin. Then, we trained a learning to rank model using the information extracted from these projects. To validate our model, we verify whether these recommendations made by them correspond to real migrations. The results showed our approach modestly outperforms random approaches. Since most Android applications are written in Java, we conclude that our approach may significantly impact Android applications' development. Therefore, we consider this result is the first step into long-term research towards a model capable of predicting precisely file-level migration, establishing the initial baseline on file migrations.

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Section: Related Workmentioning

confidence: 99%

An experience-based recommendation system to support migrations of Android applications from Java to Kotlin

Mateus,

Martinez,

Kolski

2021

Preprint

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“…In a component-based engineering process, a design pattern (D.P.) for recurrent problems solution for improving the development process for the common situation in all phases [37], [42], [48]- [52]. A DP is optimal to general issue solution that the development team faced during CBSE development process improvement [49], [50].…”

Section: Introductionmentioning

confidence: 99%

“…based on reuse in the same situations for designing systems and have different types of D.P., i.e., adapter, template, observer, strategy, etc. suitable in different scenarios [48], [49], [51], [52]. Therefore, D.P.…”

Section: Introductionmentioning

confidence: 99%

Towards Pattern-Based Change Verification Framework for Cloud-Enabled Healthcare Component-Based

et al. 2020

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To survive in the competitive environment, most organizations have adopted componentbased software development strategies in the rapid technology advancement era and the proper utilization of cloud-based services. To facilitate the continuous configuration, reduce complexity, and faster system delivery for higher user satisfaction in dynamic scenarios. In cloud services, customers select services from web applications dynamically. Healthcare body sensors are commonly used for diagnosis and monitoring patients continuously for their emergency treatment. The healthcare devices are connected with mobile or laptop etc. on cloud environment with network and frequently change applications. Thus, organizations rely on regression testing during changes and implementation to validate the quality and reliability of the system after the alteration. However, for a large application with limited resources and frequently change component management activities in the cloud computing environment, component-based system verification is difficult and challenging due to irrelevant and redundant test cases and faults. In this study, proposed a test case selection and prioritization framework using a design pattern to increase the faults detection rate. First, we select test cases on frequently accessed components using observer patterns and, secondly, prioritize test cases on adopting some strategies. The proposed framework was validated by an experiment and compared with other techniques (previous faults based and random priority). Hence, experimental results show that the proposed framework successfully verified changes. Subsequently, the proposed framework increases the fault detection rate (i.e., more than 90%) than previous faults based and random priority (i.e., more than 80% respectively).

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