Traceability Management with Impact Analysis in DevOps based Software Development

Rubasinghe, Iresha; Meedeniya, Dulani; Perera, Indika

doi:10.1109/icacci.2018.8554399

Cited by 14 publications

(13 citation statements)

References 24 publications

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“…Gürdür et al [4] conducted a literature review study on data quality dimensions and developed a dashboard for quality assessment using systematic guidelines; but they ignore the DevOps environment. We further found a study conducted by Rubasinghe et al [9] they work on software artifacts traceability in context of DevOps related software development environment using SAT-Analyzer V.1, ignoring concerns of data quality assessment.…”

Section: Introductionmentioning

confidence: 72%

“…Their research focuses on merging empirical rules with data quality dimensions instead of finding challenges of data quality assessment in DevOps environment. Rubasinghe et al [9] extend SAT-Analyzer V.1 tool that can establish traceability among the artifacts from the requirement gathering phase to software development life cycle in DevOps environment, instead of challenges to be resolved in data quality assessment.…”

Section: Background and Motivationmentioning

confidence: 99%

“…All security measures and automated monitoring frameworks are the major challenges; proper tools are required to maintain such scalability. Data generated in real time i.e., online development systems must keep check on data assessment while sharing data in a continuous environment of DevOps during production [9].…”

Section: A Findings Of Slr (Rq1)mentioning

confidence: 99%

“…There should be a proper lookup for such challenges and organization should measure them with their continuous deployment activities. Assessing of data quality before its further processing will helps the organization to use data with full assurance which save time and cost [9].…”

Section: B Findings Of Empirical Study (Rq2)mentioning

confidence: 99%

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Multicriteria Based Decision Making of DevOps Data Quality Assessment Challenges Using Fuzzy TOPSIS

Rafi

Akbar

et al. 2020

IEEE Access

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In current era, DevOps gain much interaction in software industry as it provides the flexible development environment. To meet the continuous development and operations, DevOps mainly focus, to integrate the data from heterogeneous source. While DevOps adoption, the quality assessment of data integrated from heterogeneous environment, is important and challenging at the same time. This study aims to identify the critical factors that could negatively impact the data quality assessment process in DevOps. We have used the systematic literature review (SLR) approach and identify a total of 13 critical challenging factors. The finding of SLR are further validated with industry experts via questionnaire survey. Finally, we have applied the Fuzzy TOPSIS approach to prioritize the investigated challenging factors with respect to their significance of DevOps data quality assessment process. The results show that analyzing data in real time, visualization of data and missing information and other invalid data are the highest ranked challenging factors which need to be addressed on priority basis, to successfully measure the quality of heterogeneous data in DevOps. We believe that the finding of this study will assist the practitioner to consider the most significant factors for measuring the quality of heterogeneous data in DevOps. INDEX TERMS DevOps data quality assessment, fuzzy TOPSIS, empirical investigation. SAIMA RAFI received the M.Sc. degree in computer science from the University of Agriculture Faisalabad, Faisalabad, Pakistan, and the M.S. degree in computer science from Government College University at Faisalabad, Faisalabad. She is currently pursuing the Ph.D. degree with the

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Section: Introductionmentioning

confidence: 72%

Section: Background and Motivationmentioning

confidence: 99%

Section: A Findings Of Slr (Rq1)mentioning

confidence: 99%

Section: B Findings Of Empirical Study (Rq2)mentioning

confidence: 99%

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Multicriteria Based Decision Making of DevOps Data Quality Assessment Challenges Using Fuzzy TOPSIS

Rafi

Akbar

et al. 2020

IEEE Access

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“…However, this approach lacks the artefact support for the entire SDLC. Subsequently, www.ijacsa.thesai.org this work has extended to support DevOps based software development [49] [50]. This approach manages the traceability among heterogeneous artefact types that are involved in a DevOps environment covering each stage in SDLC.…”

Section: B Related Studies On Traceability Managementmentioning

confidence: 99%

Traceability Establishment and Visualization of Software Artefacts in DevOps Practice: A Survey

Meedeniya¹,

Rubasinghe²,

Perera³

2019

IJACSA

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DevOps based software process has become popular with the vision of an effective collaboration between the development and operations teams that continuously integrates the frequent changes. Traceability manages the artefact consistency during a software process. This paper explores the trace-link creation and visualization between software artefacts, existing tool support, quality aspects and the applicability in a DevOps environment. As the novelty of this study, we identify the challenges that limit the traceability considerations in DevOps and suggest research directions. Our methodology consists of concept identification, state-of-practice exploration and analytical review. Despite the existing related work, there is a lack of tool support for the traceability management between heterogeneous artefacts in software development with DevOps practices. Although many existing studies have low industrial relevance, a few proprietary traceability tools have shown a high relevance. The lack of evidence of the related applications indicates the need for a generalized traceability approach. Accordingly, we conclude that the software artefact traceability is maturing and applying collaboratively in the software process. This can be extended to explore features such as artefact change impact analysis, change propagation, continuous integration to improve software development in DevOps environments.

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Method for Assessing the Potential Impact of Changes in Software Requirements of Agile Methodologies Based Projects

Amaral

Rosa

2023

Lecture Notes in Computer Science

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Traceability Management with Impact Analysis in DevOps based Software Development

Cited by 14 publications

References 24 publications

Multicriteria Based Decision Making of DevOps Data Quality Assessment Challenges Using Fuzzy TOPSIS

Multicriteria Based Decision Making of DevOps Data Quality Assessment Challenges Using Fuzzy TOPSIS

Traceability Establishment and Visualization of Software Artefacts in DevOps Practice: A Survey

Method for Assessing the Potential Impact of Changes in Software Requirements of Agile Methodologies Based Projects

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