Analysis of Software Binaries for Reengineering-Driven Product Line Architecture\^aAn Industrial Case Study

This paper describes a method for the recovering of software architectures from a set of similar (but unrelated) software products in binary form. One intention is to drive refactoring into software product lines and combine architecture recovery with run time binary analysis and existing clustering methods. Using our runtime binary analysis, we create graphs that capture the dependencies between different software parts. These are clustered into smaller component graphs, that group software parts with high interactions into larger entities. The component graphs serve as a basis for further software product line work. In this paper, we concentrate on the analysis part of the method and the graph clustering. We apply the graph clustering method to a real application in the context of automation / robot configuration software tools.Comment: In Proceedings FMSPLE 2015, arXiv:1504.0301

Cloud Storage and Bioinformatics in a private cloud deployment: Lessons for Data Intensive research

This paper describes service portability for a private cloud deployment, including a detailed case study about Cloud Storage and bioinformatics services developed as part of the Cloud Computing Adoption Framework (CCAF). Our Cloud Storage design and deployment is based on Storage Area Network (SAN) technologies, details of which include functionalities, technical implementation, architecture and user support. Experiments for data services (backup automation, data recovery and data migration) are performed and results confirm backup automation is completed swiftly and is reliable for data-intensive research. The data recovery result confirms that execution time is in proportion to quantity of recovered data, but the failure rate increases in an exponential manner. The data migration result confirms execution time is in proportion to disk volume of migrated data, but again the failure rate increases in an exponential manner. In addition, benefits of CCAF are illustrated using several bioinformatics examples such as tumour modelling, brain imaging, insulin molecules and simulations for medical training. Our Cloud Storage solution described here offers cost reduction, time-saving and user friendliness

Optimizing decomposition of software architecture for local recovery

Cataloged from PDF version of article.The increasing size and complexity of software systems has led to an amplified number of potential failures and as such makes it harder to ensure software reliability. Since it is usually hard to prevent all the failures, fault tolerance techniques have become more important. An essential element of fault tolerance is the recovery from failures. Local recovery is an effective approach whereby only the erroneous parts of the system are recovered while the other parts remain available. For achieving local recovery, the architecture needs to be decomposed into separate units that can be recovered in isolation. Usually, there are many different alternative ways to decompose the system into recoverable units. It appears that each of these decomposition alternatives performs differently with respect to availability and performance metrics. We propose a systematic approach dedicated to optimizing the decomposition of software architecture for local recovery. The approach provides systematic guidelines to depict the design space of the possible decomposition alternatives, to reduce the design space with respect to domain and stakeholder constraints and to balance the feasible alternatives with respect to availability and performance. The approach is supported by an integrated set of tools and illustrated for the open-source MPlayer software

Feature-based methodology for supporting architecture refactoring and maintenance of long-life software systems

Zusammenfassung Langlebige Software-Systeme durchlaufen viele bedeutende Veraenderungen im Laufe ihres Lebenszyklus, um der Weiterentwicklung der Problemdomaenen zu folgen. Normalerweise ist es schwierig eine Software-Systemarchitektur den schnellen Weiterentwicklungen einer Problemdomaene anzupassen und mit der Zeit wird der Unterschied zwischen der Problemdomaene und der Software-Systemarchitektur zu groß, um weitere Softwareentwicklung sinnvoll fortzufuehren. Fristgerechte Refactorings der Systemarchitektur sind notwendig, um dieses Problem zu vermeiden. Aufgrund des verhaeltnismaeßig hohen Gefahrenpotenzials und des zeitlich stark verzoegerten Nutzens von Refactorings, werden diese Maßnahmen normalerweise bis zum letztmoeglichen Zeitpunkt hinausgeschoben. In der Regel ist das Management abgeneigt Architektur-Refactorings zu akzeptieren, außer diese sind absolut notwendig. Die bevorzugte Vorgehensweise ist, neue Systemmerkmale ad hoc hinzuzufuegen und nach dem Motto ”Aendere nie etwas an einem funktionierenden System!” vorzugehen. Letztlich ist das Ergebnis ein Architekturzerfall (Architekturdrift). Die Notwendigkeit kleiner Refactoring-Schritte fuehrt zur Notwendigkeit des Architektur-Reengineerings. Im Gegensatz zum Refactoring, das eine normale Entwicklungstaetigkeit darstellt, ist Reengineering eine Form der Software- ”Revolution”. Reengineeringprojekte sind sehr riskant und kostspielig. Der Nutzen des Reengineerings ist normalerweise nicht so hoch wie erwartet. Wenn nach dem Reengineering schließlich die erforderlichen Architekturaenderungen statt.nden, kann dies zu spaet sein. Trotz der enormen in das Projekt gesteckten Bemuehungen erfuellen die Resultate des Reengineerings normalerweise nicht die Erwartungen. Es kann passieren, dass sehr bald ein neues, kostspieliges Reengineering erforderlich wird. In dieser Arbeit werden das Problem der Softwareevolution und der Zerfall von Softwarearchitekturen behandelt. Eine Methode wird vorgestellt, welche die Softwareentwicklung in ihrer entscheidenden Phase, dem Architekturrefactoring, unterstuetzt. Die Softwareentwicklung wird sowohl in technischer als auch organisatorischer Hinsicht unterstuetzt. Diese Arbeit hat neue Techniken entwickelt, welche die Reverse-Engineering-, Architecture-Recovery- und Architecture-Redesign-Taetigkeiten unterst uetzen. Sie schlaegt auch Aenderungen des Softwareentwicklungsprozesses vor, die fristgerechte Architekturrefactorings erzwingen koennen und damit die Notwendigkeit der Durchfuehrung eines Architektur- Reengineerings vermeiden. In dieser Arbeit wird die Merkmalmodellierung als Hauptinstrument verwendet. Merkmale werden genutzt, um die Abstraktionsluecke zwischen den Anforderungen der Problemdomaene und der Systemarchitektur zu fuellen. Merkmalmodelle werden auch als erster Grundriss fr die Wiederherstellung der verlorenen Systemarchitektur genutzt. Merkmalbasierte Analysen fuehren zu diversen, nuetzlichen Hinweisen fuer den erneuten Entwurf (das Re-Design) einer Architektur. Schließlich wird die Merkmalmodellierung als Kommunikationsmittel zwischen unterschiedlichen Projektbeteiligten (Stakeholdern) im Verlauf des Softwareengineering-Prozesses verwendet und auf dieser Grundlage wird ein neuer Anforderungsde.nitionsprozess vorgeschlagen, der die erforderlichen Architekturrefactorings erzwingt.The long-life software systems withstand many significant changes throughout their life-cycle in order to follow the evolution of the problem domains. Usually, the software system architecture can not follow the rapid evolution of a problem domain and with time, the diversion of the architecture in respect to the domain features becomes prohibiting for software evolution. For avoiding this problem, periodical refactorings of the system architecture are required. Usually, architecture refactorings are postponed until the very last moment, because of the relatively high risk involved and the lack of short-term profit. As a rule, the management is unwilling to accept architecture refactorings unless they become absolutely necessary. The preferred way of working is to add new system features in an ad-hoc manner and to keep the rule ”Never touch a running system!”. The final result is an architecture decay. The need of performing small refactoring activities turns into need for architecture reengineering. In contrast to refactoring, which is a normal evolutionary activity, reengineering is a kind of software ”revolution”. Reengineering projects are risky and expensive. The effectiveness of reengineering is also usually not as high as expected. When finally after reengineering the required architecture changes take place, it can be too late. Despite the enormous invested efforts, the results of the reengineering usually do not satisfy the expectations. It might happen that very soon a new expensive reengineering is required. This thesis deals with the problem of software evolution and the decay of software architectures. It presents a method, which assists software evolution in its crucial part, the architecture refactoring. The assistance is performed for both technical and organizational aspects of the software evolution. The thesis provides new techniques for supporting reverse engineering, architecture recovery and redesigning activities. It also proposes changes to the software engineering process, which can force timely architecture refactorings and thus avoid the need of performing architecture reengineering. For the work in this thesis feature modeling is utilized as a main asset. Features are used to fill the abstraction gap between domain requirements and system architecture. Feature models are also used as an outline for recovering of lost system architectures. Through feature-based analyses a number of useful hints and clues for architecture redesign are produced. Finally, feature modeling is used as a communication between different stakeholders of the software engineering process and on this basis a new requirements engineering process is proposed, which forces the needed architecture refactorings

Identification-method research for open-source software ecosystems

In recent years, open-source software (OSS) development has grown, with many developers around the world working on different OSS projects. A variety of open-source software ecosystems have emerged, for instance, GitHub, StackOverflow, and SourceForge. One of the most typical social-programming and code-hosting sites, GitHub, has amassed numerous open-source-software projects and developers in the same virtual collaboration platform. Since GitHub itself is a large open-source community, it hosts a collection of software projects that are developed together and coevolve. The great challenge here is how to identify the relationship between these projects, i.e., project relevance. Software-ecosystem identification is the basis of other studies in the ecosystem. Therefore, how to extract useful information in GitHub and identify software ecosystems is particularly important, and it is also a research area in symmetry. In this paper, a Topic-based Project Knowledge Metrics Framework (TPKMF) is proposed. By collecting the multisource dataset of an open-source ecosystem, project-relevance analysis of the open-source software is carried out on the basis of software-ecosystem identification. Then, we used our Spectral Clustering algorithm based on Core Project (CP-SC) to identify software-ecosystem projects and further identify software ecosystems. We verified that most software ecosystems usually contain a core software project, and most other projects are associated with it. Furthermore, we analyzed the characteristics of the ecosystem, and we also found that interactive information has greater impact on project relevance. Finally, we summarize the Topic-based Project Knowledge Metrics Framework

A Functional Architecture Approach to Neural Systems

The technology for the design of systems to perform extremely complex combinations of real-time functionality has developed over a long period. This technology is based on the use of a hardware architecture with a physical separation into memory and processing, and a software architecture which divides functionality into a disciplined hierarchy of software components which exchange unambiguous information. This technology experiences difficulty in design of systems to perform parallel processing, and extreme difficulty in design of systems which can heuristically change their own functionality. These limitations derive from the approach to information exchange between functional components. A design approach in which functional components can exchange ambiguous information leads to systems with the recommendation architecture which are less subject to these limitations. Biological brains have been constrained by natural pressures to adopt functional architectures with this different information exchange approach. Neural networks have not made a complete shift to use of ambiguous information, and do not address adequate management of context for ambiguous information exchange between modules. As a result such networks cannot be scaled to complex functionality. Simulations of systems with the recommendation architecture demonstrate the capability to heuristically organize to perform complex functionality

Architectural Layer Recovery for Software System Understanding and Evolution

This paper presents an approach to identify software layers for the understanding and evolution of software systems implemented with any object-oriented programming language. The approach first identifies relations between the classes of a software system and then uses a link analysis algorithm (i.e. the Kleinberg algorithm) to group them into layers. Additionally to assess the approach and the underlying techniques, the paper also presents a prototype of a supporting tool and the results from a case study