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

Hyun Santiago, Hurtado Julio Ariel

Foundations of Computing and Decision Sciences

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2023

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Vol. 48, No. 4

401--423

EN

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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How good are my search strings? Reflections on using an existing review as a quasi-gold standard

Tran Huynh Khanh Vi, Börstler Jürgen, bin Ali Nauman, Unterkalmsteiner Michael

e-Informatica Software Engineering Journal

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2022

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Vol. 16, nr 1

art. no. 220103

EN

Background: Systematic literature studies (SLS) have become a core research methodology in Evidence-based Software Engineering (EBSE). Search completeness, i.e., finding all relevant papers on the topic of interest, has been recognized as one of the most commonly discussed validity issues of SLSs. Aim: This study aims at raising awareness on the issues related to search string construction and on search validation using a quasi-gold standard (QGS). Furthermore, we aim at providing guidelines for search string validation. Method: We use a recently completed tertiary study as a case and complement our findings with the observations from other researchers studying and advancing EBSE. Results: We found that the issue of assessing QGS quality has not seen much attention in the literature, and the validation of automated searches in SLSs could be improved. Hence, we propose to extend the current search validation approach by the additional analysis step of the automated search validation results and provide recommendations for the QGS construction. Conclusion: In this paper, we report on new issues which could affect search completeness in SLSs. Furthermore, the proposed guideline and recommendations could help researchers implement a more reliable search strategy in their SLSs.

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Empirical AI Transformation Research: A Systematic Mapping Study and Future Agenda

Peretz-Andersson Einav, Torkar Richard

e-Informatica Software Engineering Journal

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2022

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Vol. 16, nr 1

art. no. 220108

EN

Background: Intelligent software is a significant societal change agent. Recent research indicates that organizations must change to reap the full benefits of AI. We refer to this change as AI transformation (AIT). The key challenge is to determine how to change and which are the consequences of increased AI use. Aim: The aim of this study is to aggregate the body of knowledge on AIT research. Method: We perform an systematic mapping study (SMS) and follow Kitchenham’s procedure. We identify 52 studies from Scopus, IEEE, and Science Direct (2010–2020). We use the Mixed-Methods Appraisal Tool (MMAT) to critically assess empirical work. Results: Work on AIT is mainly qualitative and originates from various disciplines. We are unable to identify any useful definition of AIT. To our knowledge, this is the first SMS that focuses on empirical AIT research. Only a few empirical studies were found in the sample we identified. Conclusions: We define AIT and propose a research agenda. Despite the popularity and attention related to AI and its effects on organizations, our study reveals that a significant amount of publications on the topic lack proper methodology or empirical data.

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Empirical Studies on Software Product Maintainability Prediction: A Systematic Mapping and Review

Elmidaoui Sara, Cheikhi Laila, Idri Ali, Abran Alain

e-Informatica Software Engineering Journal

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2019

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Vol. 13, nr 1

141--202

EN

Background: Software product maintainability prediction (SPMP) is an important task to control software maintenance activity, and many SPMP techniques for improving software maintainability have been proposed. In this study, we performed a systematic mapping and review on SPMP studies to analyze and summarize the empirical evidence on the prediction accuracy of SPMP techniques in current research. Objective: The objective of this study is twofold: (1) to classify SPMP studies reported in the literature using the following criteria: publication year, publication source, research type, empirical approach, software application type, datasets, independent variables used as predictors, dependent variables (e.g. how maintainability is expressed in terms of the variable to be predicted), tools used to gather the predictors, the successful predictors and SPMP techniques, (2) to analyze these studies from three perspectives: prediction accuracy, techniques reported to be superior in comparative studies and accuracy comparison of these techniques. Methodology: We performed a systematic mapping and review of the SPMP empirical studies published from 2000 up to 2018 based on an automated search of nine electronic databases. Results: We identified 82 primary studies and classified them according to the above criteria. The mapping study revealed that most studies were solution proposals using a history-based empirical evaluation approach, the datasets most used were historical using object-oriented software applications, maintainability in terms of the independent variable to be predicted was most frequently expressed in terms of the number of changes made to the source code, maintainability predictors most used were those provided by Chidamber and Kemerer (C&K), Li and Henry (L&H) and source code size measures, while the most used techniques were ML techniques, in particular artificial neural networks. Detailed analysis revealed that fuzzy & neuro fuzzy (FNF), artificial neural network (ANN) showed good prediction for the change topic, while multilayer perceptron (MLP), support vector machine (SVM), and group method of data handling (GMDH) techniques presented greater accuracy prediction in comparative studies. Based on our findings SPMP is still limited. Developing more accurate techniques may facilitate their use in industry and well-formed, generalizable results be obtained. We also provide guidelines for improving the maintainability of software.

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A Systematic Mapping Study on Software Measurement Programs in SMEs

Tahir T., Rasool G., Noman M.

e-Informatica Software Engineering Journal

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2018

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Vol. 12, nr 1

133--165

EN

Context: Software measurement programs are essential to understand, evaluate, improve and predict the software processes, products and resources. However, successful implementation of software measurement programs (MPs) in small and medium enterprises (SMEs) is challenging. Objective: To perform a detailed analysis of studies on MPs for highlighting the existing measurement models, tools, metrics selection methods and challenges for implementing MPs in SMEs. Methods: A Systematic Mapping Study (SMS) is conducted. Results: In total, 35 primary studies are comprehensively analyzed. We identified 29 software measurement models and 4 tools specifically designed for MPs in SMEs. Majority of the measurement models (51%) are built upon software process improvement approaches. With respect to measurement purposes of models, the distribution of MPs was identified as: characterization (63%), evaluation (83%), improvement (93%) and prediction (16%). Majority of primary studies discussed the use of measurement experts and experience (60%) followed by the use of measurement standards (40% and the use of automated tools (22%) for metrics selection in MPs. We found that the SMEs and large organization face different challenges as studies in SMEs report challenges that exist even before the implementation of MPs due to infrastructure and management processes of SMEs. The challenges reported by studies in large organizations are mostly related to the issues discovered while implementing MPs. Conclusion: The analysis of measurement models, tools, metrics selection methods and challenges of implementing MPs should help the SMEs to make a feasibility study before implementing a MP.

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Model Driven Web Engineering: A Systematic Mapping Study

Wakil K, Jawawi D N A

e-Informatica Software Engineering Journal

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2015

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Vol. 9, nr 1

107--142

EN

Background: Model Driven Web Engineering (MDWE) is the application of the model driven paradigm to the domain of web software development, where it is particularly helpful because of the continuous evolution of Web technologies and platforms. Objective: In this paper, we prepare a survey of primary studies on MDWE to explore current work and identify needs for future research. Method: Systematic mapping study uses for finding the most relevant studies and classification. In this study, we found 289 papers and a classification scheme divided them depending on their research focus, contribution type and research type. Results: The papers of solution proposal (20%) research type are majority. The most focused areas of MDWE appear to be: Web Applicability (31%), Molding and Notation (19%), and Services and Oriented (18%). The majority of contributions are methods (33%). Moreover, this shows MDWE as a wide, new, and active area to publications. Conclusions: Whilst additional analysis is warranted within the MDWE scope, in literature, composition mechanisms have been thoroughly discoursed. Furthermore, we have witnessed that the recurrent recommendation for Validation Research, Solution Proposal and Philosophical Papers has been done through earlier analysis.