BLINKER: A Blockchain-Enabled Framework for Software Provenance

Bose, R. P. Jagadeesh Chandra; Phokela, Kanchanjot Kaur; Kaulgud, Vikrant; Podder, Sanjay

doi:10.1109/apsec48747.2019.00010

Cited by 18 publications

(13 citation statements)

References 21 publications

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“…Bubbles' sizes represent the frequency of studies that fall within these intersections (The total number of studies in this map overcomes the number of selected studies, because one study, in specific, [30] refers to more than one topic). Inspired by SWEBOK, we classified the selected studies into 8 categories, namely, (i) software requirements [43], (ii) software engineering process [30,[44][45][46], (iii) software testing [47,48], (iv) software quality [49][50][51], (v) software maintenance [29], (vi) software configuration management [30,41,52], (vii) software engineering management [37,42,[53][54][55][56], and (viii) professional practice [57][58][59]. We briefly describe the SE applications below.…”

Section: Experience Studymentioning

confidence: 99%

“…Regulations and best practices, e.g., libraries should not be used if their vulnerability score is 5 and above (vulnerability threshold is 4), are encoded in the form of smart contracts [53]. Additionally, the framework enables the analysis of provenance through services, such as provenance query services that can focus on agents, artifacts or the process, and inference services to uncover non-trivial insights [54]. Finally, the framework provides services that generate composite cryptographic hashes of artifacts as the result of the concatenation of the hashes based on metadata and hashes based on content.…”

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confidence: 99%

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Software Engineering Applications Enabled by Blockchain Technology: A Systematic Mapping Study

2021

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The novel, yet disruptive blockchain technology has witnessed growing attention, due to its intrinsic potential. Besides the conventional domains that benefit from such potential, such as finance, supply chain and healthcare, blockchain use cases in software engineering have emerged recently. In this study, we aim to contribute to the body of knowledge of blockchain-oriented software engineering by providing an adequate overview of the software engineering applications enabled by blockchain technology. To do so, we carried out a systematic mapping study and identified 22 primary studies. Then, we extracted data within the research type, research topic and contribution type facets. Findings suggest an increasing trend of studies since 2018. Additionally, findings reveal the potential of using blockchain technologies as an alternative to centralized systems, such as GitHub, Travis CI, and cloud-based package managers, and also to establish trust between parties in collaborative software development. We also found out that smart contracts can enable the automation of a variety of software engineering activities that usually require human reasoning, such as the acceptance phase, payments to software engineers, and compliance adherence. In spite of the fact that the field is not yet mature, we believe that this systematic mapping study provides a holistic overview that may benefit researchers interested in bringing blockchain to the software industry, and practitioners willing to understand how blockchain can transform the software development industry.

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Section: Experience Studymentioning

confidence: 99%

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confidence: 99%

Software Engineering Applications Enabled by Blockchain Technology: A Systematic Mapping Study

2021

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“…The incentive mechanism enabled by blockchain technology eliminates the need for project leaders to assign tasks to developers; instead of that developers compete for creating the best code. Other SE researchers have proposed the use of blockchain as a backbone of the SDLC ecosystem [114][115][116], while Singi et al [114] presented a blockchain-enabled governance framework to ensure the trustworthiness of the software development process. The framework monitors and captures event data and assesses their adherence to regulations and best practices by means of smart contracts.…”

Section: Blockchain Applicabilitymentioning

confidence: 99%

What have we learnt from the challenges of (semi‐) automated requirements traceability? A discussion on blockchain applicability

2021

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Over the last 3 decades, researchers have attempted to shed light into the requirements traceability problem by introducing tracing tools, techniques, and methods with the vision of achieving ubiquitous traceability. Despite the technological advances, requirements traceability remains problematic for researchers and practitioners. This study aims to identify and investigate the main challenges in implementing (semi-)automated requirements traceability, as reported in the recent literature. A systematic literature review was carried out based on the guidelines for systematic literature reviews in software engineering, proposed by Kitchenham. We retrieved 4530 studies by searching five major bibliographic databases and selected 70 primary studies. These studies were analysed and classified according to the challenges they present and/or address. Twenty-one challenges were identified and were classified into five categories. Findings reveal that the most frequent challenges are technological challenges, in particular, low accuracy of traceability recovery methods. Findings also suggest that future research efforts should be devoted to the human facet of tracing, to explore traceability practices in organisational settings, and to develop traceability approaches that support agile and DevOps practices. Finally, it is recommended that researchers leverage blockchain technology as a suitable technical solution to ensure the trustworthiness of traceability information in interorganisational software projects.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…A SP from the industry is presented and explains that the model allows sharing SP provenance data, in addition to providing inferences and insights about these data. The applicability of the PROV-SwProcess model in sharing SP provenance data can also be validated as presented by Bose et al [2019]. This related work presents BLINKER, an extensible framework that implements/uses the PROV-SwProcess model in a blockchainbased conceptual framework for capturing, storing, exploring, and analyzing software provenance data.…”

Section: Final Remarksmentioning

confidence: 99%

“…Provenance can be divided into two types: (i) prospective provenance that captures a computational task's specification and corresponds to the steps that must be followed to generate a data product, and (ii) retrospective provenance that captures the steps executed as well as information about the environment used to derive a specific data product [FREIRE et al, 2008]. Tracking provenance enables sharing, discovering, and reusing data, simplifying collaborative activities in a GSD scenario, in addition to reproducing how something like a build failure was generated, for example [BOSE et al, 2019]. Besides that, a provenance model enables "inter-operable interchange of provenance information in heterogeneous environments such as the Web" [GROTH and MOREAU, 2013].…”

Section: Introductionmentioning

confidence: 99%

A Proposal for Sharing Software Process Provenance Data in Heterogeneous Environments

Costa¹,

Teixeira²,

Werner³

et al. 2021

Anais Do I Workshop De Práticas De Ciência Aberta Para Engenharia De Software (OpenScienSE 2021)

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Software development practices have evolved, and new approaches have emerged, like Global Software Development (GSD). In addition, software development companies started to adopt data-driven practices in parts of their business. However, using and sharing software process data in a distributed and heterogeneous environment, like the GSD context, could be a challenging topic for many software engineers. In this paper, we present a proposal for sharing software process provenance data using a model that extends PROV, the PROV- SwProcess model. An example of applying this model using a process from the industry that deals with error handling and the implementation of new features in an Enterprise Resource Planning system is presented and explains how the model allows sharing software process provenance data, in addition to providing inferences and insights about these data.

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BLINKER: A Blockchain-Enabled Framework for Software Provenance

Cited by 18 publications

References 21 publications

Software Engineering Applications Enabled by Blockchain Technology: A Systematic Mapping Study

Software Engineering Applications Enabled by Blockchain Technology: A Systematic Mapping Study

What have we learnt from the challenges of (semi‐) automated requirements traceability? A discussion on blockchain applicability

A Proposal for Sharing Software Process Provenance Data in Heterogeneous Environments

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