situ-f: a domain specific language and a first step towards the realization of situ framework

Situ proposes a human centered, dynamic reasoning framework for domain experts to evolve their software. It formally models the relationship between externally observed situation sequences and the rapid evolution of that software system, using real-time usage information from users and contextual capturing on the behavior of a software system relative to its runtime environment. Situf is a continuing effort under Situ framework. In this effort, a domain specific, functional programming language named Situf is presented from its design, semantics and a feasibility test through theoretical validation. The targeted users of this language mainly include domain experts and engineers who are versed in the major concepts and paradigms regarding human-centric situations. As argued there, human-centric situations are vitally important to infer a user\u27s intention and therefore, to drive software service evolution. Situf is designed particularly to encourage domain experts and engineers to think and work with situations. An attribute grammar based approach is developed so that through Situf , relevant real-time contexts can be systematically aggregated around situations. A computational semantics is offered to precisely describe the runtime behavior of a Situf program. While the Situf language serves as the critical centerpiece of this work, its functioning necessarily requires environmental support from Situ elements outside the language itself, such that altogether they give rise to a Situ oriented system. This environment, named Situf -based environment, is also introduced

Detection of new intentions from users for software service evolution in human-centric context-aware environments using Conditional Random Fields

The capability to accurately and efficiently obtain users’ new requirements is critical for software evolution, so that timely improvements can be made to systems to adapt to the rapidly changing environment. However, current software evolution cycles are often undesirably long because the elicitation of new requirements is mostly based on system performance or delayed user feedback and slow-paced manual analysis of requirements engineers. In this thesis, I propose a general methodology that employs Conditional Random Fields (CRF) as the mathematical foundation to provide quantitative exploration of users’ new intentions that often indicate their new requirements. My methodology is supposed to be applicable in context-aware software environments, and beneficial for discovering new requirements sooner and considerably shortening software evolution cycles. First of all, a situation-centric specification language – SiSL, is proposed to formalize the concepts and ontology of the application domains of our methodology. In SiSL, the domain of discourse is divided into five sorts of entities: action, desire, object, situation and situation-sequence. Another two important concepts, context and intention, are defined based on the five basic entities. A set of axioms are proposed to explain the relations among action, context values and desires. Based on the concepts and axioms in SiSL, a domain knowledge base which can completely describe and specify user’s behaviors and desires in human-centric context-aware environments can be constructed. To infer a user’s desire based on a peculiar form of observations and a specific detection mechanism for user’s new intentions, which may imply new requirements, the Conditional Random Fields (CRF) method is applied as a mathematical foundation to support my research work. In this thesis, the main part of a CRF model, a set of feature functions, specify the relations between observations (actions and context values) and human internal mental states (desires). To infer user’s desires, the CRF model accepts a sequence of observations as the input and calculates the score for each possible sequence-labeling, and outputs the sequence-labeling with the highest score as the inferred desire sequence. By using the CRF method, more accurate desire inference, the precondition for new intention detection, can be achieved compared with other statistical methods. To detect users’ potential new intentions, a CRF model which encodes users’ standard behavior patterns should be built as the metrics for outlier detection. The training data for building the standard CRF model are collected from observing user behaviors that are expected to conform to the system design. In the result of desire inference using the CRF model, the divergent behaviors will be labeled with desires in low confidence, and they can be singled out and analyzed for eliciting user’s potentially new intentions. Besides the divergent behaviors, user’s desire transitions and erroneous behaviors will also be analyzed for detecting new requirements or system drawbacks. The detected potential user’s new intention will be verified, analyzed and summarized to generate a formally new intention, which will drive system evolution through modifications or acquiring new functionalities to satisfy the new requirements. An experiment on a research library system has been conducted to demonstrate how to apply our methodology in detection of users’ new intentions and driving system evolution. Finally, this thesis discusses the threats to validity for our methodology and experiment