Renuka Sindhgatta

SOMA-ME: a platform for the model-driven design of SOA solutions

The service-oriented modeling and architecture modeling environment (SOMA-ME) is first a framework for the model-driven design of service-oriented architecture (SOA) solutions using the service-oriented modeling and architecture (SOMA) method. In SOMA-ME, Unified Modeling Language (UML™) profiles extend the UML 2.0 metamodel to domain-specific concepts. SOMA-ME is also a tool that extends the IBM Rational® Software Architect product to provide a development environment and automation features for designing SOA solutions in a systematic and model-driven fashion. Extensibility, traceability, variation-oriented design, and automatic generation of technical documentation and code artifacts are shown to be some of the properties of the SOMA-ME tool.

Measuring the Quality of Service Oriented Design

Service Oriented Architecture (SOA) has gained popularity as a design paradigm for realizing enterprise software systems through abstract units of functionality called services. While the key design principles of SOA have been discussed at length in the literature, much of the work is prescriptive in nature and do not explain how adherence to these principles can be quantitatively measured in practice. In some cases, metrics for a limited subset of SOA quality attributes have been proposed, but many of these measures have not been empirically validated on real-life SOA designs. In this paper, we take a deeper look at how the key SOA quality attributes of service cohesion, coupling, reusability, composability and granularity may be evaluated, based only on service design level information. We survey related work, adapt some of the well-known software design metrics to the SOA context and propose new measures where needed. These measures adhere to mathematical properties that characterize the quality attributes. We study their applicability on two real-life SOA design models from the insurance industry using a metrics computation tool integrated with an Eclipse-based service design environment. We believe that availability of these measures during SOA design will aid early detection of design flaws, allow different design options and trade-offs to be considered and support planning for development, testing and governance of the services.

SmartDispatch: enabling efficient ticket dispatch in an IT service environment

In an IT service delivery environment, the speedy dispatch of a ticket to the correct resolution group is the crucial first step in the problem resolution process. The size and complexity of such environments make the dispatch decision challenging, and incorrect routing by a human dispatcher can lead to significant delays that degrade customer satisfaction, and also have adverse financial implications for both the customer and the IT vendor. In this paper, we present SmartDispatch, a learning-based tool that seeks to automate the process of ticket dispatch while maintaining high accuracy levels. SmartDispatch comes with two classification approaches - the well-known SVM method, and a discriminative term-based approach that we designed to address some of the issues in SVM classification that were empirically observed. Using a combination of these approaches, SmartDispatch is able to automate the dispatch of a ticket to the correct resolution group for a large share of the tickets, while for the rest, it is able to suggest a short list of 3-5 groups that contain the correct resolution group with a high probability. Empirical evaluation of SmartDispatch on data from 3 large service engagement projects in IBM demonstrate the efficacy and practical utility of the approach.

An extensible framework for tracing model evolution in SOA solution design

Existing tools for model-driven development support automated change management across predefined models with precisely known dependencies. These tools cannot be easily applied to scenarios where we have a diverse set of models and relationships, and where human judgment and impact analysis are critical to introducing and managing changes. Such scenarios arise in model-based development of service oriented architectures (SOA), where a plethora of high-level models representing different aspects of the business (requirements, processes, data) need to be translated into service models, and changes across these models need to be carefully analyzed and propagated. To support the process of model evolution, we present an extensible framework that can automatically identify possible changes in any MOF-compliant model. Changes across different model types can be easily related through a user interface and via rules that are programmed at specified plug-in points. At runtime, when an instance of a model is changed, the framework performs fine-grained analysis to identify impacted models and elements therein. It also allows analysts to selectively apply or reject changes based on the specific context and summarizes the incremental impact on downstream elements as choices are made. We share our experience in using our framework during the design of a SOA-based system that underwent several changes in business models, necessitating changes in the associated service design.

A collaborative platform for application knowledge management in software maintenance projects

In the era of global outsourcing, maintenance and enhancement activities are performed in distributed locations. In most cases, the domain expertise is not available which increases the complexity to manifold. A critical success factor in such a scenario is to have a collaborative platform for managing and sharing the domain specific knowledge across distributed locations. In our ongoing research we have developed a human assisted collaborative knowledge sharing tool called CollabDev. The aim of this tool is to analyze applications in multiple languages and render various structural, architectural, and functional insights to the people involved in maintenance. The novelty of this platform lies in integrating different elements of application knowledge by linking them to source code and allowing multiple developers to collaborate on-line by using annotations for the knowledge elements. The platform also provides diagnostic information on architecture of source code.

Software evolution in agile development: a case study

The agile development method (ADM) is characterized by continuous feedback and change, and a software system developed using ADMevolves continuously through short iterations. Empirical studies on evolution of software following agile development method have been sparse. Most studies on software evolution have been performed on systems built using traditional (waterfall) development methods or using the open source development approach. This paper summarizes our study on the evolution of an enterprise software system following ADM. We evaluated key characteristics of evolution in the light of Lehmans laws of software evolution dealing with continuous change and growth, self-regulation and conservation, increasing complexity and declining quality. Our study indicates that most laws of evolution are followed by the system. We also present our observations on agile practices such as collective code ownership, test driven development and collaboration when the team is distributed.

Evolution of developer collaboration on the jazz platform: a study of a large scale agile project

Collaboration is a key aspect of the agile philosophy of software development. As a software system matures over iterations, trends of developer collaboration can offer valuable insights into project dynamics. In this paper, we study evolution of developer collaboration for a large scale agile project on the Jazz platform. We construct networks of collaboration based on developer affiliations across comments on work items and file changes; and then compare parameters of such networks with established results from networks of scientific collaborations. The comparisons illuminate interesting facets of developer collaboration on the Jazz platform. Such perception helps deeper understanding of the role of interaction in agile projects, as well as more effective project governance.

Identifying domain expertise of developers from source code

We are interested in identifying the domain expertise of developers of a software system. A developer gains expertise on the code base as well as the domain of the software system he/she develops. This information forms a useful input in allocating software implementation tasks to developers. Domain concepts represented by the system are discovered by taking into account the linguistic information available in the source code. The vocabulary contained in source code as identifiers such as class, method, variable names and comments are extracted. Concepts present in the code base are identified and grouped based on a well known text processing hypothesis - words are similar to the extent to which they share similar words. The developers association with the source code and the concepts it represents is arrived at using the version repository information. In this line, the analysis first derives documents from source code by discarding all the programming language constructs. KMeans clustering is further used to cluster documents and extract closely related concepts. The key concepts present in the documents authored by the developer determine his/her domain expertise. To validate our approach we apply it on large software systems, two of which are presented in detail in this paper.

Behavioral Analysis of Service Delivery Models

Enterprises and IT service providers are increasingly challenged with the goal of improving quality of service while reducing cost of delivery. Effective distribution of complex customer workloads among delivery teams served by diverse personnel under strict service agreements is a serious management challenge. Challenges become more pronounced when organizations adopt ad-hoc measures to reduce operational costs and mandate unscientific transformations. This paper simulates different delivery models in face of complex customer workload, stringent service contracts, and evolving skills, with the goal of scientifically deriving design principles of delivery organizations. Results show while Collaborative models are beneficial for highest priority work, Integrated models works best for volume-intensive work, through up-skilling the population with additional skills. In repetitive work environments where expertise can be gained, these training costs are compensated with higher throughput. This return-on-investment is highest when people have at most two skills. Decoupled models work well for simple workloads and relaxed service contracts.

Analysis of operational data for expertise aware staffing

Knowledge intensive business services such as IT Services, rely on the expertise of the knowledge workers for performing the activities involved in the delivery of services. The activities performed could range from performing simple, repetitive tasks to resolving more complex situations. The expertise of the task force can also vary from novices who cost less to advanced skill workers and experts who are more expensive. Staffing of service systems relies largely on the assumptions underlying the operational productivity of the workers. Research independently points to the impact of factors such as complexity of work and expertise of the worker on worker productivity. In this paper, we examine the impact of complexity of work, priority or importance of work and expertise of the worker together, on the operational productivity of the worker. For our empirical analysis, we use the data from real-life engagement in the IT service management domain. Our finding, on the basis of the data indicates, not surprisingly, that experts are more suitable for complex or high priority work with strict service levels. In the same setting, when experts are given simpler tasks of lower priority, they tend to not perform better than their less experienced counterparts. The operational productivity measure of experts and novices is further used as an input to a discrete event simulation based optimization framework that model real-life service system to arrive at an optimal staffing. Our work demonstrates that data driven techniques, similar to the one presented here is useful for making more accurate staffing decisions by understanding worker efficiency derived from the analysis of operational data.