A tactic-centric approach for automating traceability of quality concerns

Mirakhorli,; Shin, Jae Hoon; Cleland-Huang,; Çinar,

doi:10.1109/icse.2012.6227153

Cited by 70 publications

(73 citation statements)

References 23 publications

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“…Tran et al, for example, define and use a metamodel supporting different types of links for traceability between system models at different levels of abstraction (Tran et al 2011). Mirakhorli et al propose an approach to automatically reconstruct trace links between architecture tactics and classes in code (Mirakhorli et al 2014(Mirakhorli et al , 2012. They apply information retrieval and machine learning techniques for training a classifier that identifies classes related to tactics based on code snippets and tactic descriptions.…”

Section: Existing Approachesmentioning

confidence: 99%

Architecture consistency: State of the practice, challenges and requirements

Alí

Baker

O’Crowley³

et al. 2017

Empir Software Eng

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Architecture Consistency (AC) aims to align implemented systems with their intended architectures. Several AC approaches and tools have been proposed and empirically evaluated, suggesting favourable results. In this paper, we empirically examine the state of practice with respect to Architecture Consistency, through interviews with nineteen experienced software engineers. Our goal is to identify 1) any practises that the companies these architects work for, currently undertake to achieve AC; 2) any barriers to undertaking explicit AC approaches in these companies; 3) software development situations where practitioners perceive AC approaches would be useful, and 4) AC tool needs, as perceived by practitioners. We also assess current commercial AC tool offerings in terms of these perceived needs. The study reveals that many practitioners apply informal AC approaches as there are barriers for adopting more formal and Empir Software Eng (2018) 23:224-258 DOI 10.1007 The original version of this article was revised due to a retrospective Open Access order. Karlstad University, Karlstad, Sweden explicit approaches. These barriers are: 1) Difficulty in quantifying architectural inconsistency effects, and thus justifying the allocation of resources to fix them to senior management, 2) The near invisibility of architectural inconsistency to customers, 3) Practitioners' reluctance towards fixing architectural inconsistencies, and 4) Practitioners perception that huge effort is required to map the system to the architecture when using more formal AC approaches and tools. Practitioners still believe that AC would be useful in supporting several of the software development activities such as auditing, evolution and ensuring quality attributes. After reviewing several commercial tools, we posit that AC tool vendors need to work on their ability to support analysis of systems made up of different technologies, that AC tools need to enhance their capabilities with respect to artefacts such as services and meta-data, and to focus more on non-maintainability architectural concerns.

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Section: Existing Approachesmentioning

confidence: 99%

Architecture consistency: State of the practice, challenges and requirements

Alí

Baker

O’Crowley³

et al. 2017

Empir Software Eng

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“…Manually creating trace links is a difficult and expensive task, therefore we utilized a novel approach for automating the generation of trace links between tactics and the design and code elements in which they are implemented [21]. The proposed approach uses and trains a classifier to detect architectural tactics in the source code and establish trace links to the tTPs.…”

Section: B Automate Trace Generationmentioning

confidence: 99%

Preventing erosion of architectural tactics through their strategic implementation, preservation, and visualization

Mirakhorli

2013

2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Nowadays, a successful software production is increasingly dependent on how the final deployed system addresses customers' and users' quality concerns such as security, reliability, availability, interoperability, performance and many other types of such requirements. In order to satisfy such quality concerns, software architects are accountable for devising and comparing various alternate solutions, assessing the trade-offs, and finally adopting strategic design decisions which optimize the degree to which each of the quality concerns is satisfied. Although designing and implementing a good architecture is necessary, it is not usually enough. Even a good architecture can deteriorate in subsequent releases and then fail to address those concerns for which it was initially designed. In this work, we present a novel traceability approach for automating the construction of traceabilty links for architectural tactics and utilizing those links to implement a change impact analysis infrastructure to mitigate the problem of architecture degradation. Our approach utilizes machine learning methods to detect tactic-related classes. The detected tactic-related classes are then mapped to a Tactic Traceability Pattern. We train our trace algorithm using code extracted from fifty performance-centric and safety-critical open source software systems and then evaluate it against a real case study.

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“…To date these have scaled due to size limits (for example, there are around 50 tactics in our wiki). Much like the NFR framework [13], the wiki we have created is a set of common tactics [14], [15] for design solutions to important quality attributes. These tactics start with a quality attribute requirement, and are generic and not domain-specific, so they are broadly applicable.…”

Section: Populating Qa Templatesmentioning

confidence: 99%

Using AI to model quality attribute tradeoffs

Ernst

Gorton

2014

2014 IEEE 1st International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)

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Many AI techniques have been applied to goaloriented requirements engineering. However, such techniques have focused mostly on the intellectual challenge and ignored the engineering challenge of RE at scale. We discuss some of these existing approaches. We then introduce some early work that aims to add contextual quality attribute information to leverage the power of AI techniques and tools with real-world engineering. We believe this will address some of the acquisition and context problems that have plagued AI in RE.Index Terms-requirements, analysis, agile I. USING AI TO SOLVE THE REQUIREMENTS PROBLEMThis paper describes our use of AI in modeling requirements, and introduces an approach to understanding system context using AI techniques. We have immediate experience with applying AI to modeling and analyzing requirements. This seems natural, since both AI and RE are concerned with modeling the world. In particular, in our work (e.g., [1]) we have applied satisfiability solving (SAT), abductive inference for diagnosis [2] and local search [3], among others, to the problem of representing and reasoning over the requirements problem. We formulate the requirements problem as satisfying the condition W, S R, where W is domain assumptions, S is a specification, and W ∪ S is consistent and is some logical deduction relationship. In Techne [4] we represent requirements R as consisting of goals G and softgoals/quality attributes Q. Quality attributes are desired values of non-binary measurable properties of a system-to-be.This representation positions the populated requirements knowledge base ∆ as a container for well-formed formulas and assertions that can be used to answer questions for both the problem and solution domain. This is very similar to knowledge modeling for AI proposed by Levesque [5], among others. It also builds on RML [6] and Telos [7]. AI work in theories of abductive reasoning for diagnosis [2] and planning [8] is incredibly useful for finding satisfiable models for system design. In Menzies [9] these concepts were applied to solving requirements optimization challenges. The common concern here is the treatment of a requirements problem as a knowledge base, much like any other expert system might, with suitable operators for extracting knowledge using ASK style questions. For example, in Ernst et al.[1] we introduced an operator called MINIMAL GOAL ACHIEVEMENT to find the minimal set of goals to satisfy high-level requirements.We are currently applying these AI-derived goal-modeling approaches to understanding how quality attributes (such as security, performance, maintainability) guide possible software architectures and designs. In particular, we are interested in properly sizing and allocating quality attribute specific work to incremental iterations. However, there are three challenges we see in using AI to enhance RE acquisition, modeling and analysis: 1) Overcoming scalability problems of standard AI approaches. 2) Adapting AI knowledge representation techniques to RE problems. 3) Reducing a...

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A tactic-centric approach for automating traceability of quality concerns

Cited by 70 publications

References 23 publications

Architecture consistency: State of the practice, challenges and requirements

Architecture consistency: State of the practice, challenges and requirements

Preventing erosion of architectural tactics through their strategic implementation, preservation, and visualization

Using AI to model quality attribute tradeoffs

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