rΣ: Automated Reasoning Tool for Non-Functional Requirement Goal Models

Bo Wei∗, Bin Yin†, Zhi Jin‡, and Didar Zowghi§∗Academy of Math. and Systems Science, Chinese Academy of Sciences, Beijing, China. weibo@amss.ac.cn†Academy of Math. and Systems Science, Chinese Academy of Sciences, Beijing, China. ybziwen@163.com

‡Key Lab. of High Confidence Software Techno., Ministry of Edu., Peking University, Beijing, China. zhijin@amss.ac.cn§Faculty of Engineering and IT., University of Technology, Sydney, Australia. Didar.Zowghi@uts.edu.au

Abstract—Reasoning is critical for non-functional require-ments (NFRs) analysis and verification. Furthermore, it canprovide rationale about implementation strategies for NFRs.The existing tools can execute an interactive reasoning processwhich sometimes needs extra information from stakeholders.We build a tool called rΣ for reasoning on NFR modelsespecially when extra information is unavailable or forbidden,like at the model verification stage. This tool employs theformula style model as the input, automatically promotesthe reasoning process till the root node, and returns all thesatisficing statuses and the complete rationale as the output.We have applied rΣ into the real practice and to evaluate itsefficiency.

Keywords-automated reasoning tool; formula model; non-functional requirements; satisficing status;

I. Introduction

Reasoning on non-functional requirement (NFR) goalmodels is an important topic in the research community.For example, strategic rationale in i∗/TROPOS [1], [2],label propagation in the NFR Framework[3] and qualita-tive/quantatitive/hybrid analysis in GRL[4]. The reasoningmachineries in these approaches (and their supporting toolslike RE-Tools1) are interactive. Reasoning actions are usu-ally stimulated by the explicit information from stakeholdersafter interactions. The question is whether these forms ofinteractions are always available. Sometimes, interactionwith stakeholders is difficult because they have nothingmore to add to what has already been elicited, or it isimpossible to communicate with them. Sometimes, eventhough interactions are available, reasoning process willbe time-consuming because interactions will become morefrequent for large numbers of nodes. If the reasoning mech-anism can accept the implicit information and automatethe process, then the reasoning efficiency will be greatlyimproved. This paper introduces the tool which appliesClosed World Assumption to the reasoning process andsupports the automated process for NFR goal models.

II. OurWork

The reasoning tool called rΣ (pronounced as [’a: ’sigma]),is a lightweight analysis tool for NFR goal models. It isimplemented by JAVA (JDK 1.6) in Eclipse 3.2 environment,

1http://www.utdallas.edu/∼supakkul/tools/RE-Tools/index.htm

and so far supports graphical modeling, formula transforma-tion and strategy implementation.

A. Graphical ModelingrΣ inherits the main modeling idea from the NFR Frame-

work, but makes some simplification, to make it moreunderstandable and learnable. It consists of four types ofmodeling elements: 1) Softgoals including NFR softgoal,Operationalization softgoal and Claim softgoal; 2) Decom-position relationships including AND-decomposition andOR-decomposition; 3) Contribution relationships includingMAKE, BREAK, HELP, and HURT; 4) Satisficing statusesincluding fully satisficed, fully denied, weakly satisficed,weakly denied, conflict and unknown. rΣ accepts all goaltree models constructed from all of the above mentionedelements.

Figure 1. Graphical Modeling Area

B. Formula TransformationrΣ also provides the function which maps the graphical

models to the formula style models. The transformation rulesare based on the syntax of the formal modeling language Σ,which is specially for the tree style model representation[5].All graphical models in the tree style can be written asthe corresponding formulae, and we call this process “Σ-transformation”. The main benefits of using formula stylemodel are twofold. First, the formula style model is quiteconvenient to share, edit and document. The Σ formula is intextual format. It can be readable and editable everywherealthough the graphical modeling tool has not been installed.Second, the formula style model is the prerequisite ofautomated reasoning process. In our approach, automatedreasoning is done on the Σ formulae according to somereasoning rules. So formula representation is more suitablefor some matching algorithms to execute our reasoning rules.

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Figure 2. Formula Display Area

C. Strategy EvaluationStrategy evaluation is the critical analysis activity for

implementing non-functional requirements by assigning sat-isficing statuses to leaf operationalization nodes (designalternatives). rΣ adopts the closed world assumption andreasoning rules presented in [6], [7] to perform the auto-mated reasoning. To present the explicit reasoning process,rΣ will also pop up a new window to show how eachnode (highlighted in red) is used in reasoning step bystep, which is textual and easy to be documented. In thispart, rΣ allows users to optionally assign satisficing statusesfor all leaf operationalization nodes. Those unassigned leafoperationalization nodes will be assumed as “unknown”status. This can simplify the input when only selected leafoperationalization nodes are considered to be implemented.

Figure 3. Strategy Evaluation Interface

D. Other FunctionsrΣ can also support the design decision analysis and

model knowledge extension. Design decision analysismainly aims to produce a complete report about the sat-isficing statuses of non-functional requirements inferredby all possible implementation strategies[8]. While, modelknowledge extension takes advantage of all knowledge frommodels constructed to extend the target model[5]. These twofunctions are still going under improvement.

III. Case StudyWe have used rΣ to analyze the security requirements

of an enterprise financial service system supporting equitytrading in USA, Europe and Australia. Some requirementsare modeled by graphical models with more than 40 nodes,and these models are quite complicated for interactions.During the modeling process, rΣ can quickly tell us thesatisficing status, and whether the current implementationstrategy negatively impacts the performance requirements.

Figure 4. rΣ in Action

IV. Conclusion and FutureWork

rΣ is an automated reasoning tool for non-functionalrequirement goal models, which uses the formula stylemodels as the reasoning input and closed world assumptionto facilitate the automatic process. Future work includescompleting the design decision analysis and knowledgemodel extension functions. Besides, the conflict checkingamong multiple models is also a promising function of rΣ.

AcknowledgementThis work is supported by the National Grand Fun-

damental Research Program of China under Grant No.2009CB320701, the Key Projects of National Natural Sci-ence Foundation of China under Grant No. 90818026, andthe International Science Linkage Research Grant under theAustralia-China Special fund for Science and Technology.

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