Pankaj Dhoolia

Using a model-driven transformational approach and service-oriented architecture for service delivery management

IT (information technology) service providers often assume that efficient and effective service delivery can be achieved by migrating to a standard set of tools. This assumption is true only if the service provider has monolithic control over the scope and architecture of the customer environment. The trend, however, is toward selective outsourcing, customer control over the architecture of IT solutions, and retention of legacy tools. Target environments are extremely heterogeneous, and the ability of the service provider to control them is diminishing. Consequently, there is a need for a new approach to IT service workflow automation and a new generation of service-delivery management systems that support heterogeneity and collaboration. This paper introduces a new approach to automating complex and variable workflows, applies this approach to IT service delivery management (SDM), presents an SDM architecture based on this approach, and discusses an SDM implementation driven by this architecture. Our implementation architecture leverages service-oriented architecture (SOA) principles by defining loosely coupled service components and a service fulfillment pattern that dynamically integrates them. We discuss the modeling of performance metrics for service delivery and describe how the monitoring and management of key performance indicators (KPIs) are supported as an integral part of our SDM platform.

Siena: From PowerPoint to Web App in 5 Minutes

Siena lets users design web applications using commonly available PowerPoint as the modeling/development tool. From PowerPoint, users can model business artifacts and processes, transform applications to a standard representation and then immediately deploy and execute these composite applications on a model execution engine.

A RESTful Architecture for Service-Oriented Business Process Execution

This paper presents a new approach to designing business process management solutions leveraging the principles of service-oriented computing and representational state transfer. We discuss the IT artifacts that underpin this new design, illustrate the design using a real world example, and present an evaluation highlighting several desirable features of our approach.

Debugging model-transformation failures using dynamic tainting

Model-to-text (M2T) transforms are a class of software applications that translate a structured input into text output. The input models to such transforms are complex, and faults in the models that cause an M2T transform to generate an incorrect or incomplete output can be hard to debug. We present an approach based on dynamic tainting to assist transform users in debugging input models. The approach instruments the transform code to associate taint marks with the input-model elements, and propagate the marks to the output text. The taint marks identify the input-model elements that either contribute to an output string, or cause potentially incorrect paths to be executed through the transform, which results in an incorrect or a missing string in the output. We implemented our approach for XSL-based transforms and conducted empirical studies. Our results illustrate that the approach can significantly reduce the fault search space and, in many cases, precisely identify the input-model faults. The main benefit of our approach is that it automates, with a high degree of accuracy, a debugging task that can be tedious to perform manually.

Efficient Testing of Service-Oriented Applications Using Semantic Service Stubs

Service-oriented applications can be expensive to test because services are hosted remotely, are potentially shared among many users, and may have costs associated with their invocation. In this paper, we present an approach for reducing the costs of testing such applications. The key observation underlying our approach is that certain aspects of an application can be tested using locally deployed semantic service stubs, instead of actual remote services.A semantic service stub incorporates some of the service functionality, such as verifying preconditions and generating output messages based on post conditions. We illustrate how semantic stubs can enable the client test suite to be partitioned into subsets, some of which need not be executed using remote services. We also present a case study that demonstrates the feasibility of the approach, and potential cost savings for testing. The main benefits of our approach are that it can (1) reduce the number of test cases that need to be run to invoke remote services, (2) ensure that certain aspects of application functionality are well-tested before service integration occurs.

Automated support for repairing input-model faults

Model transforms are a class of applications that convert a model to another model or text. The inputs to such transforms are often large and complex; therefore, faults in the models that cause a transformation to generate incorrect output can be difficult to identify and fix. In previous work, we presented an approach that uses dynamic tainting to help locate input-model faults. In this paper, we present techniques to assist with repairing input-model faults. Our approach collects runtime information for the failing transformation, and computes repair actions that are targeted toward fixing the immediate cause of the failure. In many cases, these repair actions result in the generation of the correct output. In other cases, the initial fix can be incomplete, with the input model requiring further repairs. To address this, we present a pattern-analysis technique that identifies correct output fragments that are similar to the incorrect fragment and, based on the taint information associated with such fragments, computes additional repair actions. We present the results of empirical studies, conducted using real model transforms, which illustrate the applicability and effectiveness of our approach for repairing different types of faults.

Consultant assistant: a tool for collaborative requirements gathering and business process documentation

In this paper we present Consultant Assistant (CA), a tool to assist business consultants in collaborative requirements gathering and business process documentation. CA is a web tool that uses a model-based approach to capture the requirements. CA allows users to select relevant components of industry-specific process hierarchies, reuse documents from past engagements, collaboratively author requirements, and publish these requirements in a document based format. These documents can further be published to an asset repository for future reuse.

From informal process diagrams to formal process models

Process modeling is an important activity in business transformation projects. Free-form diagramming tools, such as PowerPoint and Visio, are the preferred tools for creating process models. However, the designs created using such tools are informal sketches, which are not amenable to automated analysis. Formal models, although desirable, are rarely created (during early design) because of the usability problems associated with formal-modeling tools. In this paper, we present an approach for automatically inferring formal process models from informal business process diagrams, so that the strengths of both types of tools can be leveraged. We discuss different sources of structural and semantic ambiguities, commonly present in informal diagrams, which pose challenges for automated inference. Our approach consists of two phases. First, it performs structural inference to identify the set of nodes and edges that constitute a process model. Then, it performs semantic interpretation, using a classifier that mimics human reasoning to associate modeling semantics with the nodes and edges. We discuss both supervised and unsupervised techniques for training such a classifier. Finally, we report results of empirical studies, conducted using flow diagrams from real projects, which illustrate the effectiveness of our approach.

AHA: Asset Harvester Assistant

Information assets in service enterprises are typically available as unstructured documents. There is an increasing need for unraveling information from these documents into a structured and semantic format. Structured data can be more effectively queried, which increases information reuse from asset repositories. This paper addresses the problem of extracting XML models, which follow a given target schema, from enterprise documents. We discuss why existing approaches for information extraction do not suffice for the enterprise documents created during service delivery. To address this limitation, we present the Asset Harvester Assistant (AHA), a tool that automatically extracts structured models from MS-Word documents, and supports manual refinement of the extracted models within an interactive environment. We present the results of empirical studies conducted using business-process documents from real service-delivery engagements. Our results indicate that the AHA approach can be effective in extracting accurate models from unstructured documents and improving user productivity.

The synergy between voting and acceptance of answers on stackoverflow, or the lack thereof

StackOverflows primary goal is to serve as a platform for users to solicit answers regarding programming questions, though its archives are often used by other users who face similar issues and thus it serves a secondary purpose of documenting common problems. The two driving mechanisms for filtering out low quality posts and highlighting the best answers are community votes and the mark of acceptance by the original question asker. But does the askers choice always match the popular vote? If so, is the askers choice influenced by the community vote or is the community vote biased towards the accepted answer? And if the asker and community disagree, then can we determine any particular characteristics of posts that influence the choice of the asker and community differently, such as its size, readability, presence of code snippets and external links as well as similarity to the original question? In this paper, we explore the answers to these questions by studying a data-set of all posts on StackOverflow from its launch in September 2008 to September 2014.