Software Architecture Design Approach

softw be package computer computer computer computer computer architecture foundation ApproachRizwan Umaid Ali1 ease up and Test as a parcel computer architecture endeavor Approach1.1 approximately the WriterLen Bass from the softw ar program Engineering Institute, CMU. Published in European company on softw ar program computer architecture 2009.1.2 IntroductionSoftw be computer architecture foundation has become a fundamental component of bundle program tuition life cycle. As opposite components of life cycle pilevasing the design of the architecture is important and relates instantaneously to overall theatrical role of the Software Application.1.3 ProblemTo make a Software Architecture a design decision procedure that crowd out test the design hypothesis, test woodland of it and identify issues and rank them on the bum of priority. The process entrust develop test case on apiece step of design process. This ordain result a sequential process in which f or each one design will be developed and well-tried and thus improving the overall design quality of software arranging.1.4 Design HypothesisMost designs are created in the context of an existing transcription, even so it is created from scratch and not being modified. Consider this our initial hypothesis put up come from succeeding(a) sourcesThe system we will modify or the in the raw functionality we will add.A functionally similar system.A manikin designed to provide services which will help in design process.A collection of legacy/open-source applications.1.5 Establish Test Cases afterward(prenominal) we bring way our initial hypothesis we substantiate to determine how to identify if design satisfies the quality benchmark expected from the application. For this we hold back to establish test cases and identify trinity sources for it. advert perspectives which can be utilize to generate test cases.Identify architecturally significant wants.View special use cases. A minute of use cases can be derived by thinking about particular(prenominal) architectural put one acrosss.1.6 Test ProcedureHaving the test cases of design hypothesis, following methods can be used to test the design and detect its shortcomings.Analytic manakins development quality attributes.Develop simulations of how design will support the test cases. realize prototype of initial design. Needs more effort but gives beaver result.1.7 Test Result and Next HypothesisThe test result will either show that the design hypothesis passes all tests and fulfills the quality requirement or there are shortcomings. The quality attributes these shortcomings relate to should be place first. We can use dickens approaches to alter the design.Apply architectural patterns to problems detected. map architectural tactics to address for specific quality attributes.The updated/next hypothesis will go through the above process recursively until the design with mandatory quality is achieved or the time allocated for the design process runs out.1.8 ConclusionThis radical presents a software architecture design process where we will test, confirm and update our design until it reaches the quality benchmark.The architect of the software system can use this process to identify shortcomings and make decisions for alternative design structures.2 SecArch Architecture-level Evaluation and testing for Security2.1 About the WriterSarah Al-Azzani and Rami Bahsoon from University of Birmingham. Published in Software Architecture (WICSA) and European Conference on Software Architecture (ECSA) in 2012.2.2 IntroductionSoftware architecture sit arounds or views are evaluated for espial problems early in the software development lifecycle. We can detect detailed protection vulnerabilities at this stage and get a chance to advance quality at a very low be. This paper presents methodological analysis for sensing security vulnerabilities caused by implied scenarios and lam see t o its.2.3 ProblemIncorporating multiple views of an architecture and studying the communications betwixt them and give ways analyze security concerns in con true systems. This will do by comparison between complete vs incomplete system models using two methods,one for detecting implied scenarios using behaviour models,and one for detecting race conditions using scenario diagrams.2.4 Scenario-based specificationsScenario-based specifications are based on procedural-flow through components. Each scenario explains a partial view of the concurrent system. The scenario-based model will have following three propertiesthe composing of scenarios from multiple component views of the software system,the attainable continuations between multiple scenario andthe concealed implied scenarios.2.5 Implied ScenariosImplied scenarios can be formed my dynamically combining two different scenarios together and provide an architectural flow for them is state fitation. Below is an typeface of beha vior model which is combining two different scenarios together. It uses an incremental algorithmic program for detecting inconsistent implied scenarios from sequence models.Figure 1 behavior model compositors case2.6 Detecting step on it ConditionsWe can apply race condition scenarios to above model and identify security vulnerabilities. Below are the 3 possible cases. Race Condition 1 disabling the innkeeper during authentication. Race Condition 2 what happens when the user commits to buy an item trance the server is being disabled. Race Condition 3 what happens when the server is disabled while the user is logging off.Below are sequence diagrams for these three race conditions.Figure 2 Race Conditions2.7 ConclusionThis paper presented an incremental architecture evaluation method that merges behavior models with structural analysis for cleansed detection of inconsistencies. We examined the concept of implied scenarios and detection of race conditions.The writer likewise com pared his proposed method with current industry practices and tested the on industry projects. He found that his method can give better results. The future work will pore on generating test cases to perform live testing on the system under test.3 Towards a Generic Architecture for Multi-Level Modeling3.1 About the WriterThomas Aschauer, Gerd Dauenhauer, Wolfgang Pree from University of Salzburg. Published in European Conference on Software Architecture 2009.3.2 IntroductionSoftware architecture good example frameworks are essential for representing architecture and their views and the viewpoints they are derived from.Conventional modeling approaches like UML do not have sufficient complexity to explain the models and meta-models (defining the models) of architecture.3.3 Problem familiar purpose meta-models are used in the conventional modeling techniques, which are not sufficient for modern software models. Model driven architecture has to use more generic approach to describe mul tilevel architecture.3.4 model-driven engineering and parameter generationModel-driven engineering (MDE) is method for managing complexities of developing large software intensive systems. The models in MDE are the main artifacts describing a system firing under design process. This paper aims at developing a framework for model-driven generation of automation system var. parameters using a testbed platform.The configuration parameters for the automation system can be generated automatically when a testbed model includes hardware and software components.Figure 3 Testbed configuration MDE3.5 Presented Prototypical carrying into actionThe below example explain the modeling approach presented in this paper. region is an example of the fixed meta-model elements represented as code in the environment. contrary types of engines can now be either initiated using the Component, or by cloning the initial Engine and copying t to radical engine.In the example, the Engine has two attribute s, Inertia and MaxSpeed. In prototypical approach each element is an instance and must provide values to these attributes. Diesel and Otto represent two kinds of engines since they are cloned from Engine, they receive copies of the attributes Inertia and MaxSpeed, as well as their values. Italics script is used to mark such copied attributes grey text is used to express that the attribute values are kept unchanged.Figure 4 Meta-models exampleIn Figure 4 DType represents a family of diesel engines. D1 lastly is a concrete, physically existing member.3.6 ConclusionThis paper we presented applications of multi-level modeling in the domain of testbed automation systems and why conventional modeling is insufficient for our MDE requirements and how multi-level modeling can solve the representation issues. They presented an approach to represent models in a great deal more detail with simple notations.4 Automated reliability prophecy from formal architectural descriptions4.1 About the Wr iterJo ao M. Franco, Raul Barbosa and M ario Zenha-Rela University of Coimbra, Portugal. Published in Software Architecture (WICSA) and European Conference on Software Architecture (ECSA) in 2012.4.2 IntroductionAssessment of quality attributes (i.e., non-functional requirements, such as performance, safe or reliability) of software architectures during design phase so early decisions are validated and the quality requirements are achieved.4.3 ProblemThese quality requirements are approximately often manually checked, which is time consuming and error-prone due to the overwhelmingly complexity of designs.A new approach to assess the reliability of software architectures. It consists in extracting and validating a Markov model from the system specification indite in an Architecture Description Language (ADL).4.4 Reliability Prediction exerciseThere are many different methods to achieve reliability farsightedness are known, each targeting diverse failure behaviours and different reliability judgement methods. The writer presented the below process for reliability omen.Architecture and Module assignment and their interactions.The Probability of Failure specified in terms of a percentage. combining the architecture with the failure behaviour. Below is an example of batch sequential musical mode state model using the Marov model.Figure 5 Markov model exampleValidation of the ProcessThe validation of the process presented by the writer was done in two steps robustness of Reliability PredictionValidity with different architectural styles.The validations were compared to previous research studies. It was found that results were similar proving that the numeral models were accurate.5 In Search of a Metric for Managing Architectural skillful Debt5.1 About the WriterRobert L. Nord and Ipek Ozkaya from the Software Engineering Institute, CMU. Published in European Conference on Software Architecture 2009.5.2 IntroductionThe technical debt is trade-off between short-term and long-term value. Taking shortcuts to optimize the delivery of features in the short term incurs debt, analogous to financial debt, that must be remunerative off later to optimize long-term victor. This paper demonstrates a architecture focused and measurement based approach to calculate technical debt by describing an application under development.5.3 ProblemTechnical debt thoroughly relays on system evaluation. An geological formation which has to evolve its system has to make sure if future development will not increase its debt and have a light cost. In this paper the writer develops a metric that assists in strategically managing technical debt.5.4 Architecture Debt AnalysisWe will analyze technical debt on two different paths. Both paths have different priorities.Path 1 assume soon.To deliver a working version of the system quickly, the plan calls for making the minimum required effort at the beginning.Path 2 contract rework and enable compatibility.Requir es an investment in infrastructure during the first deliveries. woo compression of both paths is illustrated in the table below. display board 1 terms ComparisonWe can calculate the total cost T with a function fetching implementation cost and rework cost as input.T = F( Ci, Cr)For simplicity we consider the function sums both the cost up only. We can now compare the total cost with the cumulative cost.Table 2 Cost comparison with cumulative cost5.5 Modeling remouldIn immediate software development an important challenge is to give value to long term goals then short term. The cost of taking an architectural design decision today always has a demean cost than refactoring the design in future implementations.An organization should have the following prospective towards its technical debt.Focusing on short term goals puts the organization technical jeopardy, when the debt cannot be further handled.Using shortcuts can give success on short term until the rework be starts to come a nd the cost and timeline becomes unmanageable.The architectural decisions requires active follow-ups and continuous cost analysis. This is to make sure that the design decision will make an impact in future costs of development.5.6 ConclusionFrom this research we conclude that the future development of well-designed application has lower cost and is less tentative. Therefore the technical debt in lower if the architecture is well defined and fulfills quality attributes requirement.6 Research Topic Testing Software Architectural Changes and adapting best practices to achieve highest quality in a quantifiable manner.6.1 IntroductionWe have looked into testing methodologies and design process and possible technical debt on software architecture. We now look how our technical debt will be effected if due t future requirements the architecture have to be changed.6.2 Proposed Research ProblemWe will first Estimating Technical debt onExistingSoftware architecture and Software system. Then using Design changes and code changes for estimating technical debt and quality attributes. The prediction is made based on comparisons with similar change bursts that occurred in the Architecture. The views of software architecture will be used. This is applicable in Agile Development.6.3 Types of changesWe can classify each type of change in architecture by analyzing the overall impact of it on the architecture and possibilities of technical debt from it. We also assign a propagation value to each type of debt so that its estimated suavity can be quantified. minute architectural change in one or some views.Low Technical Debt increase (0.10) accessory of new architecture. Architecture for new functionality added.Medium Technical Debt increase (0.30)Small changes in several views.High Technical Debt increase (0.60)Massive architectural change is several views.High Technical Debt increase (0.80)6.4 Proposed SolutionAfter analyzing research papers and book Software Architecture in ex pend, I can give following points on how the technical debt of new architecture can be managed.Compare updated architecture and see how the updates have increased the technical debt.Apply same test cases which were used in the initial software architecture.See how quality attributes are increased or decreased after the update.6.5 Reduction of Technical DebtTo reduce the technical debt after architectural changes following strategies can be adopted.6.5.1 RefactoringApply architectural patterns to improve several quality attributes.Use architectural tactics to address for specific quality attributes.6.5.2 Retaining existing Architecture ModelsContinue the existing architecture in patterns.Search for Modifiability tactics already used. Stick to that tactics.7 References1 Len Bass Generate and test as a software architecture design approach. WICSA/ECSA 2009 rogue 309 312.2 Sarah Al-Azzani and Rami Bahsoon. SecArch Architecture-level Evaluation and Testing for Security. In 2012 Joint W orking IEEE/IFIP Conference on Software Architecture (WICSA) and European Conference on Software Architecture (ECSA), pages 51 60, Aug. 2012.3 Thomas Aschauer, Gerd Dauenhauer, Wolfgang Pree. Towards a Generic Architecture for Multi-Level Modeling. European Conference on Software Architecture 2009 Page 121 130.4 J. Franco, R. Barbosa, and M. Zenha-Rela. Automated reliability prediction from formal architectural descriptions. In 2012 Joint Working IEEE/IFIP Conference on Software Architecture (WICSA) and European Conference on Software Architecture (ECSA), pages 302 -309, Aug. 2012.5 R. Nord, I. Ozkaya, P. Kruchten, and M. Gonzalez-Rojas, In search of a metric for managing architectural technical debt, in 2012 Joint Working IEEE/IFIP Conference on Software Architecture and sixth European Conference on Software Architecture, 2012, pp. 91-100.