Kamran Sartipi

Dynamic Analysis of Software Systems using Execution Pattern Mining

Software system analysis for extracting system functionality remains as a major problem in the reverse engineering literature and the early approaches mainly rely on static properties of software. In this paper, we propose a novel technique for dynamic analysis of software systems to identify the implementation of the software features that are specified through a number of feature-specific task scenarios. The execution of task scenarios and application of data mining algorithm sequential pattern discovery on the generated traces allow us to extract common functionality associated with the corresponding feature-specific task scenarios. The extracted patterns are used to identify the groups of core functions that implement software features. The proposed approach can be used for program comprehension and feature to source code assignment. A case study on the Unix Xfig drawing tool has been provided

HL7 FHIR: An Agile and RESTful approach to healthcare information exchange

This research examines the potential for new Health Level 7 (HL7) standard Fast Healthcare Interoperability Resources (FHIR, pronounced “fire”) standard to help achieve healthcare systems interoperability. HL7 messaging standards are widely implemented by the healthcare industry and have been deployed internationally for decades. HL7 Version 2 (“v2”) health information exchange standards are a popular choice of local hospital communities for the exchange of healthcare information, including electronic medical record information. In development for 15 years, HL7 Version 3 (“v3”) was designed to be the successor to Version 2, addressing Version 2s shortcomings. HL7 v3 has been heavily criticized by the industry for being internally inconsistent even in its own documentation, too complex and expensive to implement in real world systems and has been accused of contributing towards many failed and stalled systems implementations. HL7 is now experimenting with a new approach to the development of standards with FHIR. This research provides a chronicle of the evolution of the HL7 messaging standards, an introduction to HL7 FHIR and a comparative analysis between HL7 FHIR and previous HL7 messaging standards.

Software architecture recovery based on pattern matching

This paper is a summary of the authors thesis that presents a model and an environment for recovering the high level design of legacy software systems based on user defined architectural patterns and graph matching techniques. In the proposed model, a high-level view of a software system in terms of the system components and their interactions is represented as a query, using a description language. A query is mapped onto a pattern-graph, where a component and its interactions with other components are represented as a group of graph-nodes and a group of graph-edges, respectively. Interaction constraints can be modeled by the description language as a part of the query. Such a pattern-graph is applied against an entity-relation graph that represents the information extracted from the source code of the software system. An approximate graph matching process performs a series of graph transformation operations (i.e., node/edge insertion/deletion) on the pattern-graph and uses a ranking mechanism based on data mining association to obtain a sub-optimal solution. The obtained solution corresponds to an extracted architecture that complies with the given query.

Architectural design recovery using data mining techniques

The paper presents a technique for recovering the high level design of legacy software systems according to user defined architectural plans. Architectural plans are represented using a description language and specify system components and their interfaces. Such descriptions are viewed as queries that are applied on a large database which stores information extracted from the source code of the subject legacy system. Data mining techniques and a modified branch and bound search algorithm are used to control the matching process, by which the query is satisfied and query variables are instantiated. The matching process allows the alternative results to be ranked according to data mining associations and clustering techniques and, finally, be presented to the user.

A graph pattern matching approach to software architecture recovery

This paper presents a technique for recovering the high level design of legacy software systems based on pattern matching and user defined architectural patterns. Architectural patterns are represented using a description language that is mapped to an attributed relational graph and allows to specify the legacy system components and their data and control flow interactions. Such pattern descriptions are viewed as queries that are applied against an entity-relation graph that represents information extracted from the source code of the software system. A multi-phase branch and bound search algorithm with a forward checking mechanism controls the matching process of the two graphs by which, the query is satisfied and its variables are instantiated. An association based scoring mechanism is used to rank the alternative results generated by the matching process. Experimental results of applying the technique on the Xfig system are also presented.

Component clustering based on maximal association

Presents a supervised clustering framework for recovering the architecture of a software system. The technique measures the association between the system components (such as files) in terms of data and control flow dependencies among the groups of highly related entities that are scattered throughout the components. The application of data mining techniques allows us to extract the maximum association among the groups of entities. This association is used as a measure of closeness among the system files in order to collect them into subsystems using an optimization clustering technique. A two-phase supervised clustering process is applied to incrementally generate the clusters and control the quality of the system decomposition. In order to address the complexity, issues, the whole clustering space is decomposed into subspaces based on the association property. At each iteration, the subspaces are analyzed to determine the most eligible subspace for the next cluster, which is then followed by an optimization search to generate a new cluster.

A pattern matching framework for software architecture recovery and restructuring

The paper presents a framework for software architecture recovery and restructuring. The user specifies a high level abstraction view of the system using a structured pattern language. A pattern matching engine provides an optimal match between the given pattern and a decomposition of the legacy system entities by satisfying the inter/intramodule constraints defined by the pattern. The data mining technique Apriori is used by the matching engine to reveal meaningful data and control flow properties of the target system and limit the search space. A branch and bound search algorithm using a score function, models the constraints in the pattern as a Valued Constraint Satisfaction Problem (VCSP), and assists in searching for an optimal match between the given pattern and the target system.

On modeling software architecture recovery as graph matching

This paper presents a graph matching model for the software architecture recovery problem. Because of their expressiveness, the graphs have been widely used for representing both the software system and its high-level view, known as the conceptual architecture. Modeling the recovery process as graph matching is an attempt to identify a sub-optimal transformation from a pattern graph, representing the high-level view of the system, onto a subgraph of the software system graph. A successful match yields a restructured system that conforms to the given pattern graph. A failed match indicates the points where the system violates specific constraints. The pattern graph generation and the incrementality of the recovery process are the important issues to be addressed. The approach is evaluated through case studies using a prototype toolkit that implements the proposed interactive recovery environment.

Mined-Knowledge and Decision Support Services in Electronic Health

Large organizations in various information domains are constantly facing the challenges of growing size, new business requirements, and customer demands for service agility. As an example, in the healthcare domain provision of unique electronic health record systems (EHR) for patient identification and health history, integration of regional systems into a nation-wide system, information and service sharing, and security and privacy of patient data have generated a set of new challenges. Canada Health In-foway has proposed an information infrastructure for networked healthcare systems that is based on service oriented architecture (SOA) and provides standards for sharing data and services. In this paper, we investigate the provision of mined-knowledge (results of data mining on patient data), clinical decision support systems, and network visualization and monitoring through SOA. We also address the advantages of SOA implementation using an enterprise service bus in order to accommodate these services. Such services can benefit similar domains such as banking, communications, air traffic control, and transportation.

Cross-domain information and service interoperability

The growing trends towards integrating legacy applications with new systems in a network-centric environment has introduced yet another level of complexity beyond those we witnessed in development of large monolithic systems. In this context, most research challenges focus on interoperability within the same domain. However, provision of cross-domain interoperability among collaborating domains is a new challenge that needs more attention from the research community. Such interoperability requires data and service extraction to obtain common subsets of information and services in collaborating domains, e.g., healthcare and insurance. The first step in achieving such a large interoperability is to follow similar development processes for collaborating domains, which provides homogeneity in their architectures. The second step would be to provide intra-domain and inter-domain semantic interoperability through proprietary and shared ontology systems. In this paper, we address the above challenges through description of a framework that is based on core information standards and terminology systems and employs a guideline to achieve service interoperability among systems of the collaborating domains. A real-world case study of cross-domain interoperability among two domains healthcare and insurance is presented.