Past and future of software architectural decisions – A systematic mapping study

Tofan, Dan; Galster, Matthias; Avgeriou, Paris; Schuitema, Wes

doi:10.1016/j.infsof.2014.03.009

Cited by 63 publications

(20 citation statements)

References 28 publications

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“…Unfortunately, little is known about group architectural decisions, and how to improve group architectural decision making. In a recent mapping study on architectural decisions [5], we found that not much research exists on group architectural decisions. Group architectural decision making entails substantial challenges, such as communication among decision makers and the need to reach a certain degree of consensus between decision makers and other stakeholders [6].…”

Section: Problem Descriptionmentioning

confidence: 87%

“…GADGET was motivated by noticing that most architectural decisions are made in groups [3,4] and that little research exists on group architectural decisions [5]. Consensus is conceptualized in terms of its two main components: general agreement and mutual understanding.…”

Section: Discussionmentioning

confidence: 99%

“…GADGET refinements -since there is a need for treating uncertainty in architectural decision making [5], we will update GADGET to include support for uncertainty in group architectural decisions. Also, we will investigate and collect evidence on the value of using more fine-grained iterations among GADGET steps than the current iteration in step 5 of GADGET.…”

Section: Discussionmentioning

confidence: 99%

“…While investigating how architectural decisions are made in practice [3], we found out that most architectural decisions are group decisions, similar to [4]. Furthermore, one of the outcomes of a systematic mapping study on architectural decisions literature was that there is little research on group architectural decisions [5]. These outcomes motivated us to propose an approach to improve consensus in group architectural decisions in phase 2.…”

Section: Contributionsmentioning

confidence: 95%

“…Lack of a reference process. As we found out in previous research [5], there is no reference process in the literature for group architectural decision making to use as a baseline for comparison.…”

Section: Gadget Experimentsmentioning

confidence: 99%

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Empirical evaluation of a process to increase consensus in group architectural decision making

Tofan

Galster

Lytra

et al. 2016

Information and Software Technology

Self Cite

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Context: Many software architectural decisions are group decisions rather than decisions made by individuals. Consensus in a group of decision makers increases the acceptance of a decision among decision makers and their confidence in that decision. Furthermore, going through the process of reaching consensus means that decision makers understand better the decision (including the decision topic, decision options, rationales, and potential outcomes). Little guidance exists on how to increase consensus in group architectural decision making.Objective: We evaluate how a newly proposed process (named GADGET) helps architects increase consensus when making group architectural decisions. Specifically, we investigate how well GADGET increases consensus in group architectural decision making, by understanding its practical applicability, and by comparing GADGET against group architectural decision making without using any prescribed approach.Method: We conducted two empirical studies. First, we conducted an exploratory case study to understand the practical applicability of GADGET in industry. We investigated whether there is a need to increase consensus, the effort and benefits of GADGET, and potential improvements for GADGET. Second, we conducted an experiment with 113 students from three universities to compare GADGET against group architectural decision making without using any prescribed approach.Results: GADGET helps decision makers increase their consensus, captures knowledge on architectural decisions, clarifies the different points of view of different decision makers on the decision, and increases the focus of the group discussions about a decision. From the experiment, we obtained causal evidence that GADGET increases consensus better than group architectural decision making without using any prescribed approach.Conclusions: There is a need to increase consensus in group architectural decisions. GADGET helps inexperienced architects increase consensus in group architectural decision making, and provides additional benefits, such as capturing rationale of decisions. Future work is needed to understand and improve other aspects of group architectural decision making.

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Section: Problem Descriptionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 95%

Section: Gadget Experimentsmentioning

confidence: 99%

See 3 more Smart Citations

Empirical evaluation of a process to increase consensus in group architectural decision making

Tofan

Galster

Lytra

et al. 2016

Information and Software Technology

Self Cite

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Mining software architecture knowledge: Classifying stack overflow posts using machine learning

Ali

Mushtaq

Rasheed

et al. 2021

Concurrency and Computation

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Software Architectural Process (SAP) is a core and excessively knowledge intensive phase of software development life cycle, as it consumes and produces knowledge artifacts, simultaneously. SAP is about making design decisions, and the changes in these verdicts may pose adverse effects on software projects. The performance and properties of software components are fundamentally influenced by the design decisions.The implementation of immature and abrupt design decisions seriously threatens the development process of SAP. Moreover, software architectural knowledge management (AKM) approaches offer systematic ways to support SAP through versatile architectural solutions and design decisions. However, the majority of software organizations have limited access to data and still depend upon manually created and maintained AKM process. In this paper, we have utilized the one of the most prominent online community for software development (i.e., Stack Overflow) as a source of SAP knowledge to support AKM. In order to support AKM, we have proposed a supervised machine learning-based approach to classify the architectural knowledge into predefined categories, that is, analysis, synthesis, evaluation, and implementation. We have employed different combinations of feature selection technique to achieve the optimal classification results of the used classifiers (Support Vector Machine [SVM], K-Nearest Neighbor, Random Forest, and Naive Bayes [NB]). Among these classifiers, SVM with Uni-gram feature set provides best classification results and attains 85.80% accuracy.For evaluating the proposed approach's effectiveness, we have also computed the suitability of the classifiers, that is, the cost of computation along with its accuracy, and NB with Uni-gram feature set proved to be the most suitable. K E Y W O R D Sarchitectural knowledge management, stack overflow, crowd-sourced communities, text mining, classification INTRODUCTIONSoftware architectural design appears to have a significant importance in Software Development Life Cycle (SDLC). Its manifests are the early design decisions which help to determine the system development, deployment, and evaluation. To develop a quality project, better architectural decisions are essential and these decisions pose vast challenges for software engineers. Architectural Knowledge Management (AKM) is used to capture

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When rationality meets intuition: A research agenda for software design decision‐making

Pretorius,

Razavian,

Eling

et al. 2024

J Software Evolu Process

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As society's reliance on software systems escalates over time, so too does the cost of failure of these systems. Meanwhile, the complexity of software systems, as well as of their designs, is also ever‐increasing, influenced by the proliferation of new tools and technologies to address intended societal needs. The traditional response to this complexity in software engineering and software architecture has been to apply rationalistic approaches to software design through methods and tools for capturing design rationale and evaluating various design options against a set of criteria. However, research from other fields demonstrates that intuition may also hold benefits for making complex design decisions. All humans, including software designers, use intuition and rationality in varying combinations. The aim of this article is to provide a comprehensive overview of what is known and unknown from existing research regarding the use and performance consequences of using intuition and rationality in software design decision‐making. To this end, a systematic literature review has been conducted, with an initial sample of 3909 unique publications and a final sample of 26 primary studies. We present an overview of existing research, based on the literature concerning intuition and rationality use in software design decision‐making and propose a research agenda with 14 questions that should encourage researchers to fill identified research gaps. This research agenda emphasizes what should be investigated to be able to develop support for the application of the two cognitive processes in software design decision‐making.

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Past and future of software architectural decisions – A systematic mapping study

Cited by 63 publications

References 28 publications

Empirical evaluation of a process to increase consensus in group architectural decision making

Empirical evaluation of a process to increase consensus in group architectural decision making

Mining software architecture knowledge: Classifying stack overflow posts using machine learning

When rationality meets intuition: A research agenda for software design decision‐making

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