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Relevant bibliographies by topics / Software architecture reconstruction

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Academic literature on the topic 'Software architecture reconstruction'

Author: Grafiati

Published: 9 March 2023

Last updated: 11 March 2023

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Journal articles on the topic "Software architecture reconstruction"

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Schmidt, Frederick, Stephen MacDonell, and Andy M. Connor. "Multi-Objective Reconstruction of Software Architecture." International Journal of Software Engineering and Knowledge Engineering 28, no. 06 (June 2018): 869–92. http://dx.doi.org/10.1142/s0218194018500262.

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Design erosion is a persistent problem within the software engineering discipline. Software designs tend to deteriorate over time and there is a need for tools and techniques that support software architects when dealing with legacy systems. This paper presents an evaluation of a search-based software engineering (SBSE) approach intended to recover high-level architecture designs of software systems by structuring low-level artifacts into high-level architecture artifact configurations. In particular, this paper describes the performance evaluation of a number of metaheuristic search algorithms applied to architecture reconstruction problems with high dimensionality in terms of objectives. These problems have been selected as representative of the typical challenges faced by software architects dealing with legacy systems and the results inform the ongoing development of a software tool that supports the analysis of trade-offs between different reconstructed architectures.

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Stoermer, Christoph, Anthony Rowe, Liam O'Brien, and Chris Verhoef. "Model-centric software architecture reconstruction." Software: Practice and Experience 36, no. 4 (2006): 333–63. http://dx.doi.org/10.1002/spe.699.

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KANG, SUNGWON, SEONAH LEE, and DANHYUNG LEE. "A FRAMEWORK FOR TOOL-BASED SOFTWARE ARCHITECTURE RECONSTRUCTION." International Journal of Software Engineering and Knowledge Engineering 19, no. 02 (March 2009): 283–305. http://dx.doi.org/10.1142/s0218194009004167.

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For software with nontrivial size and complexity, it is not feasible to manually perform architecture reconstruction. Therefore it is essential for the software architecture miner who is mining architecture from the existing software to have a well-defined software architecture reconstruction process that helps incorporate as much tool use as possible at the appropriate steps of architecture reconstruction. There are some existing software architecture reconstruction frameworks but they do not provide guidelines on how to systematically utilize tools to produce architecture views for a reconstruction purpose. In this paper, we propose a framework for tool-based software architecture reconstruction. This framework consists of a generic process for software architecture reconstruction and the steps to derive from it a concrete tool-based process to be used for actual architecture reconstruction. The architecture miner can use this framework to analyze source code for modifying source code as well as to reconstruct software architecture from source code.

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Prajapati, Amarjeet, and Zong Woo Geem. "Harmony Search-Based Approach for Multi-Objective Software Architecture Reconstruction." Mathematics 8, no. 11 (October 31, 2020): 1906. http://dx.doi.org/10.3390/math8111906.

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The success of any software system highly depends on the quality of architectural design. It has been observed that over time, the quality of software architectural design gets degraded. The software system with poor architecture design is difficult to understand and maintain. To improve the architecture of a software system, multiple design goals or objectives (often conflicting) need to be optimized simultaneously. To address such types of multi-objective optimization problems a variety of metaheuristic-oriented computational intelligence algorithms have been proposed. In existing approaches, harmony search (HS) algorithm has been demonstrated as an effective approach for numerous types of complex optimization problems. Despite the successful application of the HS algorithm on different non-software engineering optimization problems, it gained little attention in the direction of architecture reconstruction problem. In this study, we customize the original HS algorithm and propose a multi-objective harmony search algorithm for software architecture reconstruction (MoHS-SAR). To demonstrate the effectiveness of the MoHS-SAR, it has been tested on seven object-oriented software projects and compared with the existing related multi-objective evolutionary algorithms in terms of different software architecture quality metrics and metaheuristic performance criteria. The experimental results show that the MoHS-SAR performs better compared to the other related multi-objective evolutionary algorithms.

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Qiu, Dehong, Qifeng Zhang, and Shaohong Fang. "Reconstructing Software High-Level Architecture by Clustering Weighted Directed Class Graph." International Journal of Software Engineering and Knowledge Engineering 25, no. 04 (May 2015): 701–26. http://dx.doi.org/10.1142/s0218194015500072.

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Software architecture reconstruction plays an important role in software reuse, evolution and maintenance. Clustering is a promising technique for software architecture reconstruction. However, the representation of software, which serves as clustering input, and the clustering algorithm need to be improved in real applications. The representation should contain appropriate and adequate information of software. Furthermore, the clustering algorithm should be adapted to the particular demands of software architecture reconstruction well. In this paper, we first extract Weighted Directed Class Graph (WDCG) to represent object-oriented software. WDCG is a structural and quantitative representation of software, which contains not only the static information of software source code but also the dynamic information of software execution. Then we propose a WDCG-based Clustering Algorithm (WDCG-CA) to reconstruct high-level software architecture. WDCG-CA makes full use of the structural and quantitative information of WDCG, and avoids wrong compositions and arbitrary partitions successfully in the process of reconstructing software architecture. We introduce four metrics to evaluate the performance of WDCG-CA. The results of the comparative experiments show that WDCG-CA outperforms the comparative approaches in most cases in terms of the four metrics.

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Ducasse, S., and D. Pollet. "Software Architecture Reconstruction: A Process-Oriented Taxonomy." IEEE Transactions on Software Engineering 35, no. 4 (July 2009): 573–91. http://dx.doi.org/10.1109/tse.2009.19.

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Guamán, Daniel, Jennifer Pérez, Jessica Diaz, and Carlos E. Cuesta. "Towards a reference process for software architecture reconstruction." IET Software 14, no. 6 (December 1, 2020): 592–606. http://dx.doi.org/10.1049/iet-sen.2019.0246.

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Arcelli Fontana, Francesca, and Marco Zanoni. "A tool for design pattern detection and software architecture reconstruction." Information Sciences 181, no. 7 (April 1, 2011): 1306–24. http://dx.doi.org/10.1016/j.ins.2010.12.002.

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Müller, Jan, Dirk Fimmel, Renate Merker, and Rainer Schaffer. "A Hardware–Software System for Tomographic Reconstruction." Journal of Circuits, Systems and Computers 12, no. 02 (April 2003): 203–29. http://dx.doi.org/10.1142/s021812660300074x.

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We present the design of a hardware–software system for the reconstruction of tomographic images. In a systematic approach we developed the parallel processor array, a reconfigurable hardware controller and processing kernel, and the software control up to the integration into a graphical user interface. The processor array acting as a hardware accelerator, is constructed using theoretical results and methods of application-specific hardware design. The reconfigurability of the system allows one to utilize a much wider realm of algorithms than the three reconstruction algorithms implemented so far. In the paper we discuss the system design at different levels from algorithm transformations to board development.

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Allam, Atef, and Wael Deabes. "Model-Based Hardware-Software Codesign of ECT Digital Processing Unit." Modelling and Simulation in Engineering 2021 (March 30, 2021): 1–14. http://dx.doi.org/10.1155/2021/4757464.

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Image reconstruction algorithm and its controller constitute the main modules of the electrical capacitance tomography (ECT) system; in order to achieve the trade-off between the attainable performance and the flexibility of the image reconstruction and control design of the ECT system, hardware-software codesign of a digital processing unit (DPU) targeting FPGA system-on-chip (SoC) is presented. Design and implementation of software and hardware components of the ECT-DPU and their integration and verification based on the model-based design (MBD) paradigm are proposed. The inner-product of large vectors constitutes the core of the majority of these ECT image reconstruction algorithms. Full parallel implementation of large vector multiplication on FPGA consumes a huge number of resources and incurs long combinational path delay. The proposed MBD of the ECT-DPU tackles this problem by crafting a parametric segmented parallel inner-product architecture so as to work as the shared hardware core unit for the parallel matrix multiplication in the image reconstruction and control of the ECT system. This allowed the parameterized core unit to be configured at system-level to tackle large matrices with the segment length working as a design degree of freedom. It allows the trade-off between performance and resource usage and determines the level of computation parallelism. Using MBD with the proposed segmented architecture, the system design can be flexibly tailored to the designer specifications to fulfill the required performance while meeting the resources constraint. In the linear-back projection image reconstruction algorithm, the segmentation scheme has exhibited high resource saving of 43% and 71% for a small degradation in a frame rate of 3% and 14%, respectively.

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Dissertations / Theses on the topic "Software architecture reconstruction"

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Knodel, Jens. "Process models for the reconstruction of software architecture views." [S.l. : s.n.], 2002. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10252225.

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MAGGIONI, STEFANO. "Design pattern detection and software architecture reconstruction: an integrated approach based on software micro-structures." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/7817.

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Zapalowski, Vanius. "Evaluation of code-based information to architectural module identification." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/94691.

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Arquitetura de software desempenha um importante papel no desenvolvimento de software, quando explicitamente documentada, ela melhora o entendimento sobre o sistema implementado e torna possível entender a forma com que requisitos não funcionais são tratados. Apesar da relevância da arquitetura de software, muitos sistemas não possuem uma arquitetura documentada, e nos casos em que a arquitetura existe, ela pode estar desatualizada por causa da evolução descontrolada do software. O processo de recuperação de arquitetura de um sistema depende principalmente do conhecimento que as pessoas envolvidas com o software tem. Isso acontece porque a recuperação de arquitetura é uma tarefa que demanda muita investigação manual do código fonte. Pesquisas sobre recuperação de arquitetura objetivam auxiliar esse processo. A maioria dos métodos de recuperação existentes são baseados em dependência entre elementos da arquitetura, padrões arquiteturais ou similaridade semântica do código fonte. Embora as abordagem atuais ajudem na identi cação de módulos arquiteturais, os resultados devem ser melhorados de forma signi cativa para serem considerados con áveis. Então, nesta dissertação, objetivamos melhorar o suporte a recuperação de arquitetura explorando diferentes fontes de informação e técnicas de aprendizado de máquina. Nosso trabalho consiste de uma análise, considerando cinco estudo de casos, da utilidade de usar um conjunto de características de código (features, no contexto de aprendizado de máquina) para agrupar elementos em módulos da arquitetura. Atualmente não são conhecidas as características que afetam a identificação de papéis na arquitetura de software. Por isso, nós avaliamos a relação entre diferentes conjuntos de características e a acurácia obtida por um algoritmo não supervisionado na identificação de módulos da arquitetura. Consequentemente, nós entendemos quais dessas características revelam informação sobre a organização de papéis do código fonte. Nossa abordagem usando características de elementos de software atingiu uma acurácia média significativa. Indicando a relevância das informações selecionadas para recuperar a arquitetura. Além disso, nós desenvolvemos uma ferramenta para auxílio ao processo de recuperação de arquitetura de software. Nossa ferramenta tem como principais funções a avaliação da recuperação de arquitetura e apresentação de diferentes visualizações arquiteturais. Para isso, apresentamos comparações entre a arquitetura concreta e a arquitetura sugerida.
Software architecture plays an important role in the software development, and when explicitly documented, it allows understanding an implemented system and reasoning about how non-functional requirements are addressed. In spite of that, many developed systems lack proper architecture documentation, and if it exists, it may be outdated due to software evolution. The process of recovering the architecture of a system depends mainly on developers' knowledge requiring a manual inspection of the source code. Research on architecture recovery provides support to this process. Most of the existing approaches are based on architectural elements dependency, architectural patterns or source code semantics, but even though they help identifying architectural modules, the obtained results must be signi cantly improved to be considered reliable. We thus aim to support this task by the exploitation of di erent code-oriented information and machine learning techniques. Our work consists of an analysis, involving ve case studies, of the usefulness of adopting a set of code-level characteristics (or features, in the machine learning terminology) to group elements into architectural modules. The characteristics mainly source code metrics that a ect the identi cation of what role software elements play in software architecture are unknown. Then, we evaluate the relationship between di erent sets of characteristics and the accuracy achieved by an unsupervised algorithm the Expectation Maximization in identifying architectural modules. Consequently, we are able to understand which of those characteristics reveal information about the source code structure. By the use of code-oriented information, our approach achieves a signi cant average accuracy, which indicates the importance of the selected information to recover software architecture. Additionally, we provide a tool to support research on architecture recovery providing software architecture measurements and visualizations. It presents comparisons between predicted architectures and concrete architectures.

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Le, Borgne Alexandre. "ARIANE : Automated Re-Documentation to Improve software Architecture uNderstanding and Evolution." Thesis, IMT Mines Alès, 2020. http://www.theses.fr/2020EMAL0001.

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Tout au long de son cycle de vie, un logiciel peut connaître de nombreux changements affectant potentiellement sa conformité avec sa documentation originelle. De plus, bien qu'une documentation à jour, conservant les décisions de conception prises pendant le cycle de développement, soit reconnue comme une aide importante pour maîtriser les évolutions, la documentation des logiciels est souvent obsolète. Les modèles d’architectures sont l’une des pièces majeures de la documentation. Assurer leur cohérence avec les autres modèles d’un logiciel (incluant son code) pendant les processus d’évolution (co-évolution) est un atout majeur pour la qualité logicielle. En effet, la compréhension des architectures logicielles est hautement valorisable en termes de capacités de réutilisation, d'évolution et de maintenance. Pourtant les modèles d’architectures sont rarement explicitement disponibles et de nombreux travaux de recherche visent à les retrouver à partir du code source. Cependant, la plupart des approches existantes n'effectuent pas un strict processus de rétro-documentation afin de re-documenter les architectures "comme elles sont implémentées" mais appliquent des étapes de ré-ingénierie en regroupant des éléments de code dans de nouveaux composants. Ainsi, cette thèse propose un processus de re-documentation des architectures telles qu’elles ont été conçues et implémentées, afin de fournir un support d’analyse des décisions architecturales effectives. Cette re-documentation se fait par l’analyse du code orienté objet et les descripteurs de déploiement de projets. Le processus re-documente les projets dans le langage de description d’architecture Dedal, qui est spécialement conçu pour contrôler et guider l’évolution des logiciels.Un autre aspect très important de la documentation des logiciels est le suivi de leurs différentes versions. Dans de nombreuses approches et gestionnaires de version actuels, comme Github, les fichiers sont versionnés de manière agnostique. S’il est possible de garder une trace de l’historique des versions de n’importe quel fichier, aucune information ne peut être fournie sur la sémantique des changements réalisés. En particulier, lors du versionnement d’éléments logiciels, il n’est fourni aucun diagnostic de retro-compatibilité avec les versions précédentes. Cette thèse propose donc un mécanisme de versionnement d’architectures logicielles basé sur le métamodèle et les propriétés formelles de l’ADL Dedal.Il permet d’analyser automatiquement les versions en termes de substituabilité, de gérer la propagation de version et d’incrémenter automatiquement les numéros de versions en tenant compte de l’impact des changements. En proposant cette approche formelle, cette thèse vise à prévenir le manque de contrôle des décisions architecturale (dérive / érosion).Cette thèse s’appuie sur une étude empirique appliquant les processus de re-documentation et de versionnement à de nombreuses versions d’un projet industriel extrait de GitHub
All along its life-cycle, a software may be subject to numerous changes that may affect its coherence with its original documentation. Moreover, despite the general agreement that up-to-date documentation is a great help to record design decisions all along the software life-cycle, software documentation is often outdated. Architecture models are one of the major documentation pieces. Ensuring coherence between them and other models of the software (including code) during software evolution (co-evolution) is a strong asset to software quality. Additionally, understanding a software architecture is highly valuable in terms of reuse, evolution and maintenance capabilities. For that reason, re-documenting software becomes essential for easing the understanding of software architectures. However architectures are rarely available and many research works aim at automatically recovering software architectures from code. Yet, most of the existing re-documenting approaches do not perform a strict reverse-documenting process to re-document architectures "as they are implemented" and perform re-engineering by clustering code into new components. Thus, this thesis proposes a framework for re-documentating architectures as they have been designed and implemented to provide a support for analyzing architectural decisions. This re-documentation is performed from the analysis of both object-oriented code and project deployment descriptors. The re-documentation process targets the Dedal architecture language which is especially tailored for managing and driving software evolution.Another highly important aspect of software documentation relates to the way concepts are versioned. Indeed, in many approaches and actual version control systems such as Github, files are versioned in an agnostic manner. This way of versioning keeps track of any file history. However, no information can be provided on the nature of the new version, and especially regarding software backward-compatibility with previous versions. This thesis thus proposes a formal way to version software architectures, based on the use of the Dedal architecture description language which provides a set of formal properties. It enables to automatically analyze versions in terms of substitutability, version propagation and proposes an automatic way for incrementing version tags so that their semantics corrrespond to actual evolution impact. By proposing such a formal approach, this thesis intends to prevent software drift and erosion.This thesis also proposes an empirical study based on both re-documenting and versioning processes on numerous versions on an enterprise project taken from Github

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Wilhelm, Andreas Johannes [Verfasser], Hans Michael [Akademischer Betreuer] Gerndt, Hans Michael [Gutachter] Gerndt, and Felix [Gutachter] Wolf. "Interactive Software Parallelization Based on Hybrid Analysis and Software Architecture Reconstruction / Andreas Johannes Wilhelm ; Gutachter: Hans Michael Gerndt, Felix Wolf ; Betreuer: Hans Michael Gerndt." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1185637990/34.

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Krogmann, Klaus [Verfasser], and R. [Akademischer Betreuer] Reussner. "Reconstruction of Software Component Architectures and Behaviour Models using Static and Dynamic Analysis / Klaus Krogmann ; Betreuer: R. Reussner." Karlsruhe : KIT Scientific Publishing, 2012. http://d-nb.info/1184493901/34.

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Stegmann, Korine. "Knoop." Diss., 2008. http://hdl.handle.net/2263/29863.

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The essence of this dissertation was to explore the synergies between fashion and architecture to inform the design of a resource rich environment.
Dissertation (MArch(Prof))--University of Pretoria, 2009.
Architecture
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Krogmann, Klaus [Verfasser]. "Reconstruction of software component architectures and behaviour models using static and dynamic analysis / von Klaus Krogmann." 2010. http://d-nb.info/1010373587/34.

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Books on the topic "Software architecture reconstruction"

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Brown, Melissa. Software Directory for Home Builders & Remodelers 1996. Home Builder Pr, 1995.

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Book chapters on the topic "Software architecture reconstruction"

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Koschke, Rainer. "Architecture Reconstruction." In Software Engineering, 140–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-95888-8_6.

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Riva, Claudio. "Architecture Reconstruction in Practice." In Software Architecture, 159–73. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-0-387-35607-5_10.

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Guo, George Yanbing, Joanne M. Atlee, and Rick Kazman. "A Software Architecture Reconstruction Method." In Software Architecture, 15–33. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-0-387-35563-4_2.

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Ersoy, Ersin, Kamer Kaya, Metin Altınışık, and Hasan Sözer. "Using Hypergraph Clustering for Software Architecture Reconstruction of Data-Tier Software." In Software Architecture, 326–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48992-6_24.

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Prajapati, Amarjeet, and Zong Woo Geem. "Harmony Search-Enhanced Software Architecture Reconstruction." In Handbook on Artificial Intelligence-Empowered Applied Software Engineering, 87–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08202-3_6.

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Khodamoradi, Kamyar, Jafar Habibi, and Ali Kamandi. "Architectural Styles as a Guide for Software Architecture Reconstruction." In Communications in Computer and Information Science, 985–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89985-3_149.

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Schmidt, Frederik, Stephen G. MacDonell, and Andrew M. Connor. "An Automatic Architecture Reconstruction and Refactoring Framework." In Software Engineering Research,Management and Applications 2011, 95–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23202-2_7.

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Walker, Andrew, Ian Laird, and Tomas Cerny. "On Automatic Software Architecture Reconstruction of Microservice Applications." In Lecture Notes in Electrical Engineering, 223–34. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6385-4_21.

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Rademacher, Florian, Sabine Sachweh, and Albert Zündorf. "A Modeling Method for Systematic Architecture Reconstruction of Microservice-Based Software Systems." In Enterprise, Business-Process and Information Systems Modeling, 311–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49418-6_21.

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Guamán, Daniel, Jennifer Pérez, Juan Garbajosa, and Germania Rodríguez. "A Systematic-Oriented Process for Tool Selection: The Case of Green and Technical Debt Tools in Architecture Reconstruction." In Product-Focused Software Process Improvement, 237–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64148-1_15.

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Conference papers on the topic "Software architecture reconstruction"

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Pruijt, Leo, and Wiebe Wiersema. "Software architecture reconstruction and compliance checking." In ECSA '17: 11th European Conference on Software Architecture. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3129790.3129809.

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Mirakhorli, Mehdi. "Software architecture reconstruction: Why? What? How?" In 2015 IEEE 22nd International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2015. http://dx.doi.org/10.1109/saner.2015.7081885.

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de Jong, Tijmen, and Jan Martijn E. M. van der Werf. "Process-mining based dynamic software architecture reconstruction." In the 13th European Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3344948.3344985.

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Ganesan, Dharmalingam, Mikael Lindvall, Rance Cleaveland, Raoul Jetley, Paul Jones, and Yi Zhang. "Architecture Reconstruction and Analysis of Medical Device Software." In 2011 9th Working IEEE/IFIP Conference on Software Architecture (WICSA). IEEE, 2011. http://dx.doi.org/10.1109/wicsa.2011.33.

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O'Brien, L., D. Smith, and G. Lewis. "Supporting Migration to Services using Software Architecture Reconstruction." In 13th IEEE International Workshop on Software Technology and Engineering Practice (STEP'05). IEEE, 2005. http://dx.doi.org/10.1109/step.2005.29.

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Sartipi, Kamran, Nima Dezhkam, and Hossein Safyallah. "An Orchestrated Multi-view Software Architecture Reconstruction Environment." In 2006 13th Working Conference on Reverse Engineering. IEEE, 2006. http://dx.doi.org/10.1109/wcre.2006.13.

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Johnson, C. A., S. Thada, M. Rodriguez, Y. Zhao, A. R. Iano-Fletcher, J. S. Liow, W. C. Barker, R. L. Martino, and R. E. Carson. "Software architecture of the MOLAR-HRRT reconstruction engine." In IEEE Symposium Conference Record Nuclear Science 2004. IEEE, 2004. http://dx.doi.org/10.1109/nssmic.2004.1466744.

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Stormer, Christoph. "Software Quality Attribute Analysis by Architecture Reconstruction (SQUA3RE)." In 11th European Conference on Software Maintenance and Reengineering (CSMR'07). IEEE, 2007. http://dx.doi.org/10.1109/csmr.2007.43.

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Pollet, Damien, Stephane Ducasse, Loic Poyet, Ilham Alloui, Sorana Cimpan, and Herve Verjus. "Towards A Process-Oriented Software Architecture Reconstruction Taxonomy." In 11th European Conference on Software Maintenance and Reengineering (CSMR'07). IEEE, 2007. http://dx.doi.org/10.1109/csmr.2007.50.

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Pruijt, Leo, Wiebe Wiersema, and Jan Martijn van der Werf. "Software architecture reconstruction research support as provided by HUSACCT." In ECSA '17: 11th European Conference on Software Architecture. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3129790.3129819.

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Reports on the topic "Software architecture reconstruction"

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O'Brien, Liam, Christoph Stoermer, and Chris Verhoef. Software Architecture Reconstruction: Practice Needs and Current Approaches. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada407795.

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Kazman, Rick, and S. J. Carriere. Playing Detective: Reconstructing Software Architecture from Available Evidence. Fort Belvoir, VA: Defense Technical Information Center, October 1997. http://dx.doi.org/10.21236/ada330928.

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