Grounded Architectures: Using Grounded Theory for the Design of Software Architectures

Gidey, Habtom Kahsay; Marmsoler, Diego; Eckhardt, Jonas

doi:10.1109/icsaw.2017.41

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Another validation technique for results discovered using grounded theory is a literature search [41]. The parallel definitions in the literature and our process model indicate consistency between existing studies and our process model as demonstrated in Sections 5.1 and 5.2.…”

Section: Threats To Validitysupporting

confidence: 56%

“…In open coding, we detected key points and assigned them with labels to build codes by summarizing incidents via team behaviors; the labels make concepts that support capturing and collecting the main objects, instances, incidents, and actions on the data [41].…”

Section: A Qualitative Analysismentioning

confidence: 99%

“…The credibility of this research inherits some internal limitations from the grounded theory. Grounded theory might restrict the potential of the data and the creativity of the qualitative analyst, and it may be influenced by the personality, experience, or perspectives of researchers [41], [91]. To overcome this bias and increase credibility, we collected data from multiple sources using various methods.…”

Section: Threats To Validitymentioning

confidence: 99%

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Software Project Failure Process Definition

Nizam

2022

IEEE Access

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Many researchers have attempted to identify the factors behind software project failures and their solutions from various perspectives. However, systematic and integrated process definitions of failure as process models for success are lacking. This study aims to build a process definition for software project failure as an anti-pattern by identifying the main phases and their relationships in terms of team behavior. We researched software engineering literature and case studies to gather information about critical incidents and repeating behaviors of teams in failed projects into a novel dataset. Grounded theory was employed to build a theoretical foundation for failure phase definitions from the collected data. The design structure matrix and Bayesian belief network were used for the quantitative assessment of the transitions between phases. The results revealed that common behavioral patterns occurred in approximately 89 percent of the case studies, supporting the decision to consider software project failure as a process. The proposed failure process definition has a simple structure that uses everyday concepts for phase names and reveals the critical behaviors leading a software project to failure Thus, it provides critical insights for software professionals, non-technical stakeholders, and managers to evaluate the progress of their projects and design strategies to avoid failure.INDEX TERMS Bayesian belief network, grounded theory, qualitative process analysis, software process models.

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Section: Threats To Validitysupporting

confidence: 56%

Section: A Qualitative Analysismentioning

confidence: 99%

Section: Threats To Validitymentioning

confidence: 99%

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Software Project Failure Process Definition

Nizam

2022

IEEE Access

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“…-Decision Mechanisms: agents [39], case-based reasoning, constrain-based reasoning [40], decision trees and statistical models for managing uncertainty [41], multi-criteria hierarchic optimization problem [42], grounded theory [43], decisions trees combined with reflective component models [44], or fuzzy inference systems [8], [45].…”

Section: A Software Architecturesmentioning

confidence: 99%

Classifying Model-View-Controller Software Applications Using Self-Organizing Maps

2021

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The new era of information and the needs of our society require continuous change in software and technology. Changes are produced very quickly and software systems require evolving at the same velocity, which implies that the decision-making process of software architectures should be (semi-)automated to satisfy changing needs and to avoid wrong decisions. This issue is critical since suboptimal architecture design decisions may lead to high cost and poor software quality. Therefore, systematic and (semi-)automated mechanisms that help software architects during the decision-making process are required. Architectural patterns are one of the most important features of software applications, but the same pattern can be implemented in different ways, leaving to results of different quality. When an application requires to evolve, knowledge extracted from similar applications is useful for driving decisions, since quality pattern implementations can be reproduced in similar applications to improve specific quality attributes. Therefore, clustering methods are especially suitable for classifying similar pattern implementations. In this paper, we apply a novel unsupervised clustering technique, based on the well-known artificial neural network model Self-Organizing Maps, to classify Model-View-Controller (MVC) pattern from a quality point of view. Software quality is analyzed by 24 metrics organized into the categories of Count/Size, Maintainability, Duplications, Complexity, and Design Quality. The main goal of this work is twofold: to identify the quality features that establish the similarity of MVC applications without software architect bias, and to classify MVC applications by means of Self-Organizing Maps based on quality metrics. To that end, this work performs an exploratory study by conducting two analyses with a dataset of 87 Java MVC applications characterized by the 24 metrics and two attributes that describe the technology dimension of the application. The stated findings provide a knowledge base that can help in the decision-making process for the architecture of Java MVC applications.

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“…QDA, and in particular GT, has been used to develop models [5-7, 26, 27, 50], requirements [8,10,40,45], personas [17,18,34], and architectures [3,21]. QDAcity-RE is also focused on producing models.…”

Section: Related Workmentioning

confidence: 99%

A validation of QDAcity-RE for domain modeling using qualitative data analysis

et al. 2021

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Using qualitative data analysis (QDA) to perform domain analysis and modeling has shown great promise. Yet, the evaluation of such approaches has been limited to single-case case studies. While these exploratory cases are valuable for an initial assessment, the evaluation of the efficacy of QDA to solve the suggested problems is restricted by the common single-case case study research design. Using our own method, called QDAcity-RE, as the example, we present an in-depth empirical evaluation of employing qualitative data analysis for domain modeling using a controlled experiment design. Our controlled experiment shows that the QDA-based method leads to a deeper and richer set of domain concepts discovered from the data, while also being more time efficient than the control group using a comparable non-QDA-based method with the same level of traceability.

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Grounded Architectures: Using Grounded Theory for the Design of Software Architectures

Cited by 6 publications

References 23 publications

Software Project Failure Process Definition

Software Project Failure Process Definition

Classifying Model-View-Controller Software Applications Using Self-Organizing Maps

A validation of QDAcity-RE for domain modeling using qualitative data analysis

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