Flávia A. Barros

iAIML: a Mechanism to Treat Intentionality in AIML Chatterbots

This paper presents iAIML, a mechanism to treat intentional information based on AIML, a state-of-the-art technology in chatterbot development. Our main goal was to improve dialogues with AIML chatterbots. iAIML adds structure to AIML bases, incorporating intentions and rules used in sentence interpretation and generation. We adopted as linguistic base the conversational analysis theory (CAT), which considers intentionality in adjacent pairs in dialogue, facilitating the establishment of consistent dialogues between chatterbots and users. Tests with the implemented solution showed feasibility of the proposed approach. This is an original work with several contributions, such as the innovative and effective use of CAT, and a consistent modular structure of the iAIML base, favoring reuse and maintenance

Persona-AIML: an architecture for developing chatterbots with personality

This work presents the Persona-AIML architecture for the creation of chatterbots in AIML (Artificial Intelligence Markup Language) with personality. It is a flexible architecture that allows the use of different models of personality in the construction of chatterbots. Tests with the prototype revealed satisfactory and very encouraging results.

Test case generation from natural language requirements based on SCR specifications

Formal models are increasingly used as input for automated test generation strategies. As an example, Software Cost Reduction (SCR) has been designed to detect and correct errors during the requirements phase, also allowing test generation. However, SCR syntax is not trivial for those who are unfamiliar with it. We propose here a strategy to generate test cases from natural language requirements using SCR as an intermediate and hidden formalism. To avoid textual ambiguity, the requirements are written according to a Controlled Natural Language. Each syntactically valid requirement is mapped into a semantic representation from which an SCR specification is derived. We then use the T-VEC tool to generate tests from SCR. We evaluated our strategy based on requirements and manually written test vectors provided by our partner from the Aviation Industry. Our strategy generated 85% of the vectors in the original set, with 100% of precision. The generation time was 2s. Yet, we obtained a mutation score of 84%.

Search based constrained test case selection using execution effort

Software testing is essential to guarantee high quality products. However, it is a very expensive activity, particularly when manually performed. One way to cut down costs is by reducing the input test suites, which are usually large in order to fully satisfy the test goals. Yet, since large test suites usually contain redundancies (i.e., two or more test cases (TC) covering the same requirement/piece of code), it is possible to reduce them in order to respect time/people constraints without severely compromising coverage. In this light, we formulated the TC selection problem as a constrained search based optimization task, using requirements coverage as the fitness function to be maximized (quality of the resultant suite), and the execution effort (time) of the selected TCs as a constraint in the search process. Our work is based on the Particle Swarm Optimization (PSO) algorithm, which is simple and efficient when compared to other widespread search techniques. Despite that, besides our previous works, we did not find any other proposals using PSO for TC selection, neither we found solutions treating this task as a constrained optimization problem. We implemented a Binary Constrained PSO (BCPSO) for functional TC selection, and two hybrid algorithms integrating BCPSO with local search mechanisms, in order to refine the solutions provided by BCPSO. These algorithms were evaluated using two different real-world test suites of functional TCs related to the mobile devices domain. In the performed experiments, the BCPSO obtained promising results for the optimization tasks considered. Also, the hybrid algorithms obtained statistically better results than the individual search techniques.

Model-Based Testing from Controlled Natural Language Requirements

Model-Based Testing (MBT) techniques usually take as input models that are not available in the very beginning of a development. Therefore, its use is postponed. In this work we present an approach to MBT that takes as input requirements described in a Controlled Natural Language. Initially, the requirements are syntactically analyzed according to a domain specific language for describing system requirements, and their informal semantics is depicted based on the Case Grammar theory. Then, the requirements semantics is automatically represented as a Transition Relation, which provides formal basis for MBT, and test cases are generated with the support of a solver. Our approach was evaluated considering four examples from different domains. Within seconds, our approach generated 94 % of the test vectors manually written by specialists. Moreover, considering a mutant-based strength analysis, our approach yielded a mutation score between 54 % and 98 %.

Analyzing On-Line Collaborative Dialogues: The OXEnTCHÊ–Chat

Internet-based virtual learning environments allow participants to refine their knowledge by interacting with their peers. Besides, they offer ways to escape from the isolation seen in the CAI and ITS systems. However, simply allowing participants to interact is not enough to eliminate the isolation feeling and to motivate students. Recent research in Computer Supported Collaborative Learning has been investigating ways to minor the above problems. This paper presents the OXEnTCHE–Chat, a chat tool coupled with an automatic dialogue classifier which analyses on-line interaction and provides just-in-time feedback to both instructors and learners. Feedback is provided through reports, which can be user-specific or about the whole dialogue. The tool also counts on a chatterbot, which plays the role of an automatic coordinator. The implemented prototype of OXEnTCHE–Chat has been evaluated and the obtained results are very satisfactory.

SmartChat – An Intelligent Environment for Collaborative Discussions

Using Computer Supported Collaborative Learning Environments (CSCLE) two or more participants can build their knowledge together, through reflection, collaborative problem resolution, information exchange, and decision-making. The majority of these environments provide tools for communication (e-mails, chats, and forums). However, there are no mechanisms for the evaluation of the interaction contents. The lack of a mechanism to evaluate the interactions could prevent the users from discussing about a specific theme or collaborating among themselves.

NAT2TEST Tool: From Natural Language Requirements to Test Cases Based on CSP

Formal models are increasingly being used as input for automated test-generation strategies. However, typically the requirements are captured as English text, and these formal models are not readily available. With this in mind, we have devised a strategy (NAT2TEST) to obtain formal models from natural language requirements automatically, particularly to generate sound test cases. Our strategy is extensible, since we consider an intermediate and hidden formal characterisation of the system behaviour from which other formal notations can be derived. Here, we present the NAT2TEST tool, which implements our strategy.

NAT2TEST SCR : Test case generation from natural language requirements based on SCR specifications

Formal models are increasingly being used as input for automated test generation strategies. Software Cost Reduction (SCR), for example, was designed to detect and correct errors during the requirements phase, also allowing test generation. However, the syntax of SCR and other formalisms are not trivial for non-experts. In this work, we present a strategy for test case generation from natural language requirements that uses SCR as an intermediate and hidden formalism. To minimize textual ambiguity, requirements are written according to a controlled natural language. Syntactically valid requirements are mapped into their semantic representation using case frames, from which SCR specifications are derived. These specifications are then used by the T-VEC tool to generate tests cases. Our strategy was evaluated in four different domains: (i) a vending machine (toy example); (ii) a control system for safety injection in a nuclear power plant (publicly available), (iii) one example provided by our industrial partner Embraer; and (iv) the turn indicator system of Mercedes vehicles (publicly available). As a baseline we considered random testing, and, in general, our strategy outperformed it in terms of performance and mutant-based strength analysis.

Adding Personality to Chatterbots Using the Persona-AIML Architecture

Recent studies highlight the importance of personality for improving human-machine interaction. Attempts of including personal- ity in chatterbots have not been satisfactory regarding the coherence of the chatterbots behavior, the flexibility and reusability of the person- ality model. This work presents Persona-AIML, an original architecture for the creation of chatterbots in AIML with personality. It is a flexible architecture that allows the use of different models of personality, de- scribed in terms of five elements: traits, attitudes, mood, emotions and physical states. Recent experiments validate the reusability and extensi- bility of our architecture to build chatterbots with different personalities, however using the same categories base. The implemented chatterbots demonstrated a satisfactory level of coherence in their behavior.