Towards Consistency Checking Between Software Architecture and Informal Documentation

Keim, Jan; Koziolek, Anne

doi:10.1109/icsa-c.2019.00052

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…Artificial Intelligence [35], [26], [33], [18], [8], [27], [25], [28], [11], [17], [30], [3] 12 35.29% Documentation Model [42], [38], [13], [21], [37], [45] 6 17.65%…”

Section: Papers Count Percentagementioning

confidence: 99%

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Traceability of Architectural Design Decisions and Software Artifacts: A Systematic Mapping Study

Hyun,

Hurtado

2023

Foundations of Computing and Decision Sciences

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The definition of architecture is a crucial task in software development, where the architect is responsible for making the right decisions to meet specific functional and quality requirements. These architectural design decisions form the foundation that shapes the arrangement of elements within a system. Unfortunately, these decisions are often poorly documented, implicit in various artifacts, or inadequately updated, leading to negative consequences on the maintainability of a system and resulting in rework and cost overruns. The objective of this systematic mapping study is to comprehend the current state regarding approaches for traceability of architectural design decisions and how these decisions are linked with the different artifacts used in software development. To achieve this, an information extraction protocol is followed, utilizing databases with search strings, inclusion, and exclusion criteria. The findings demonstrate that this knowledge is highly relevant; however, it is rarely explicitly documented. As a result, most works propose diverse approaches to extract this knowledge from existing technical documentation, commonly used tools, and other sources of product and process information. In contrast, it is evident that there is no standard for documenting design decisions, leading each author to present a subjective version of what is important and where to trace these decisions. This suggests that there is still a significant amount of research to be conducted regarding the traceability of these architectural design decisions and their connection with software artifacts. Such research could lead to intriguing new proposals for investigation.

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“…Artificial Intelligence [35], [26], [33], [18], [8], [27], [25], [28], [11], [17], [30], [3] 12 35.29% Documentation Model [42], [38], [13], [21], [37], [45] 6 17.65%…”

Section: Papers Count Percentagementioning

confidence: 99%

“…Technical Docs [8], [27], [25], [28], [11], [55], [49], [7], [5], [51], [13], [21], [37], [48] 14 41.18%…”

Section: Artifactunclassified

Traceability of Architectural Design Decisions and Software Artifacts: A Systematic Mapping Study

Hyun,

Hurtado

2023

Foundations of Computing and Decision Sciences

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“…This is more prominent in traceability use cases pertaining to software architecture where experiential knowledge is vital in recovering architectural trace links (Keim and Koziolek, 2019) and links between requirements and process models (Lapeña et al, 2019).…”

Section: Dependency On Tacit Knowledgementioning

confidence: 99%

Applications of Natural Language Processing in Software Traceability: A Systematic Mapping Study

Pauzi

Capiluppi

2022

SSRN Journal

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“…En [17], los autores presentan la problemática de mantener información de diferentes fuentes consistente, con coherencia entre los modelos y la documentación. Para ello, los autores abordan la comprobación de la coherencia entre modelos y artefactos de lenguaje natural textual utilizando comprensión del lenguaje natural.…”

Section: Trabajos Relacionadosunclassified

Evaluation of Markov Models for Architecture Conformance Checking

Rodríguez¹,

Armentano²,

Soria³

et al. 2020

IEEE Latin Am. Trans.

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Conformance betweenarchitecture and implementation is a key aspect of architecture-centric development. However, the architecture "as documented" and the architecture "as implemented" tend to diverge from each other over time. Thus, conformance checks should be run periodically on the system in order to detect and correct differences. Despite having a structural conformance analysis, assessing whether the main scenarios describing the architectural behavior are faithfully implemented in the code is still challenging. Checking conformance to architectural scenarios is usually a time-consuming and error-prone activity. In this article, we describe ArchLearner, a tool to assist architects to bridge the gap between architecture and its implementation. The architecture is specified with Use-Case Maps (UCMs), a notation for modeling both high-level structure and behavior. ArchLearner uses Markov Models to detect code deviations with respect to predetermined UCMs, based on the analysis of system execution traces for those UCMs. The results from two casestudies have shown that ArchLearner is practical and reduces conformance checking efforts.

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Towards Consistency Checking Between Software Architecture and Informal Documentation

Cited by 8 publications

References 21 publications

Traceability of Architectural Design Decisions and Software Artifacts: A Systematic Mapping Study

Traceability of Architectural Design Decisions and Software Artifacts: A Systematic Mapping Study

Applications of Natural Language Processing in Software Traceability: A Systematic Mapping Study

Evaluation of Markov Models for Architecture Conformance Checking

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