The accuracy of dependency analysis in static architecture compliance checking

Pruijt, Leo; Köppe, Christian; Werf, Jan Martijn E. M. van der; Brinkkemper, Sjaak

doi:10.1002/spe.2421

Cited by 16 publications

(8 citation statements)

References 49 publications

(81 reference statements)

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“…It provides developers with inconsistency awareness as they are developing through margin alerts, through auto-complete alerts and through the architectural model itself, also integrating a facility where developers can email architects with alerts when inconsistencies are introduced. In addition, a recent study by Pruijt et al (2016) has shown that JITTAC was more holistic and effective, in terms of detecting all the source code dependencies in software systems than the other academic and commercial offerings in the area.…”

Section: Existing Approachesmentioning

confidence: 99%

Architecture consistency: State of the practice, challenges and requirements

Alí

Baker

O’Crowley³

et al. 2017

Empir Software Eng

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Architecture Consistency (AC) aims to align implemented systems with their intended architectures. Several AC approaches and tools have been proposed and empirically evaluated, suggesting favourable results. In this paper, we empirically examine the state of practice with respect to Architecture Consistency, through interviews with nineteen experienced software engineers. Our goal is to identify 1) any practises that the companies these architects work for, currently undertake to achieve AC; 2) any barriers to undertaking explicit AC approaches in these companies; 3) software development situations where practitioners perceive AC approaches would be useful, and 4) AC tool needs, as perceived by practitioners. We also assess current commercial AC tool offerings in terms of these perceived needs. The study reveals that many practitioners apply informal AC approaches as there are barriers for adopting more formal and Empir Software Eng (2018) 23:224-258 DOI 10.1007 The original version of this article was revised due to a retrospective Open Access order. Karlstad University, Karlstad, Sweden explicit approaches. These barriers are: 1) Difficulty in quantifying architectural inconsistency effects, and thus justifying the allocation of resources to fix them to senior management, 2) The near invisibility of architectural inconsistency to customers, 3) Practitioners' reluctance towards fixing architectural inconsistencies, and 4) Practitioners perception that huge effort is required to map the system to the architecture when using more formal AC approaches and tools. Practitioners still believe that AC would be useful in supporting several of the software development activities such as auditing, evolution and ensuring quality attributes. After reviewing several commercial tools, we posit that AC tool vendors need to work on their ability to support analysis of systems made up of different technologies, that AC tools need to enhance their capabilities with respect to artefacts such as services and meta-data, and to focus more on non-maintainability architectural concerns.

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Section: Existing Approachesmentioning

confidence: 99%

Architecture consistency: State of the practice, challenges and requirements

Alí

Baker

O’Crowley³

et al. 2017

Empir Software Eng

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“…By use we mean any time a declares a variable of type b, invokes a method on an object of type b, accesses a field of an object of type b, or inherits from type b. The way we calculate dependencies complies with the benchmark and guidelines provided by Pruijt et al [45].…”

Section: Calculating Extentmentioning

confidence: 99%

An architectural technical debt index based on machine learning and architectural smells

Sas¹,

Avgeriou²

2023

Preprint

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A key aspect of technical debt (TD) management is the ability to measure the amount of principal accumulated in a system. The current literature contains an array of approaches to estimate TD principal, however, only a few of them focus specifically on architectural TD, and none of these are fully automated, freely available, and thoroughly validated. Moreover, a recent study has shown that many of the current approaches suffer from certain shortcomings, such as relying on hand-picked thresholds. In this paper, we propose a novel approach to estimate architectural technical debt principal based on machine learning and architectural smells to address such shortcomings. Our approach can estimate the amount of technical debt principal generated by a single architectural smell instance. To do so, we adopt novel techniques from Information Retrieval to train a learning-to-rank machine learning model that estimates the severity of an architectural smell and ensure the transparency of the predictions. Then, for each instance, we statically analyse the source code to calculate the exact number of lines of code creating the smell. Finally, we combine these two values to calculate the technical debt principal. To validate the approach, we conducted a case study and interviewed 16 practitioners, from both open source and industry, and asked them about their opinions on the TD principal estimations for several smells detected in their projects. The results show that for 71% of instances, practitioners agreed that the estimations provided were representative of the effort necessary to refactor the smell.

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“…We rely on the notion of compliance [52] between MyA and MyRA, which we implemented as virtual conformance. The conformance relationship in MDE defines a typing relationship between objects and their types.…”

Section: Model Promotion Transformationmentioning

confidence: 99%

Reference architectures modelling and compliance checking

et al. 2022

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Reference architectures (RAs) are successfully used to represent families of concrete software architectures in several domains such as automotive, banking, and the Internet of Things. RAs inspire architects when designing concrete architectures, and they help to guarantee compliance with architectural decisions, regulatory requirements, as well as architectural qualities. Despite their importance, reference architectures still suffer from a number of open technical issues, including (i) the lack of a common interpretation, a precise notation for their representation and documentation, and (ii) the lack of conformance mechanisms for checking the compliance of concrete architectures to their related reference architecture, architectural decisions, regulatory requirements, etc. This paper addresses these two issues by introducing a model-driven approach that leverages (i) a domain-independent metamodel for the representation of reference architectures and (ii) the combination of model transformation and weaving techniques for the automatic conformance checking of concrete architectures. We evaluate the applicability, effectiveness, and generalizability of our approach using illustrative examples from the web browsers and automotive domains, including an assessment from an independent practitioner.

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The accuracy of dependency analysis in static architecture compliance checking

Cited by 16 publications

References 49 publications

Architecture consistency: State of the practice, challenges and requirements

Architecture consistency: State of the practice, challenges and requirements

An architectural technical debt index based on machine learning and architectural smells

Reference architectures modelling and compliance checking

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