The paper describes the need for and goals of tool-integration within software development processes. In particular we focus on agile software development but are not limited to. The integration of tools and data between the different domains of the process is essential for an efficient, effective and customized software development. We describe what the next steps in the pursuit of integration are and how major goals can be achieved. Beyond theoretical and architectural considerations we describe the prototypical implementation of an open platform approach. The paper introduces platform apps and a functionality store as general concepts to make apps and their functionalities available to the community. We describe the implementation of the approach and how it can be practically utilized. The description is based on one major use case and further steps are motivated by various other examples.
Nevertheless, neither is integration of applications and lifecycle data a very new problem nor it is restricted to the integration of lifecycle data of applications, exclusively. Analogous to the ALM integration of lifecycle data, the integration of other lifecycle data is treated by other branches, e. ,g. for product lifecycle management (PLM). That is, the integration trend exceeds the domain of software development. Srinivasan @cite_9 describes for the domain of PLM an integration framework using standardized product data, meta-data models and standardized business processes, on the one hand, and on the other hand a service oriented architecture concept to integrate different tools and systems.
@cite_9
1983841261 2054506507 2251814753 2171262729
Abstract The need for integrating business and technical information systems, allowing partners to collaborate effectively in creating innovative products, has motivated the design and deployment of a novel integration framework for product lifecycle management. The time is ripe for such an integration framework because of the convergence of three important developments, almost in a perfect storm: (1) maturity of standardized product data and meta-data models, and standardized engineering and business processes; (2) emergence of service-oriented architecture for information sharing; and (3) availability of robust middleware to implement them. These developments allow engineering and business objects and processes to be built or composed as modular pieces of software in the form of services that can communicate with each other and be used across different parts of a business. These modular software pieces can be reused and reconfigured in new ways as business conditions change, thereby saving time and money for companies. This paper describes the business and technical aspects of an integration framework for product lifecycle management using open standards and service-oriented architecture. Automated tools for understanding application behavior and its changes during the application lifecycle are essential for many performance analysis and debugging tasks. Application performance issues have an immediate impact on customer experience and satisfaction. A sudden slowdown of enterprise-wide application can effect a large population of customers, lead to delayed projects, and ultimately can result in company financial loss. Significantly shortened time between new software releases further exacerbates the problem of thoroughly evaluating the performance of an updated application. Our thesis is that online performance modeling should be a part of routine application monitoring. Early, informative warnings on significant changes in application performance should help service providers to timely identify and prevent performance problems and their negative impact on the service. We propose a novel framework for automated anomaly detection and application change analysis. It is based on integration of two complementary techniques: (i) a regression-based transaction model that reflects a resource consumption model of the application, and (ii) an application performance signature that provides a compact model of runtime behavior of the application. The proposed integrated framework provides a simple and powerful solution for anomaly detection and analysis of essential performance changes in application behavior. An additional benefit of the proposed approach is its simplicity: It is not intrusive and is based on monitoring data that is typically available in enterprise production environments. The introduced solution further enables the automation of capacity planning and resource provisioning tasks of multitier applications in rapidly evolving IT environments. The frequently changing user preferences and or item profiles have put essential importance on the dynamic modeling of users and items in personalized recommender systems. However, due to the insufficiency of per user item records when splitting the already sparse data across time dimension, previous methods have to restrict the drifting purchasing patterns to pre-assumed distributions, and were hardly able to model them rather directly with, for example, time series analysis. Integrating content information helps to alleviate the problem in practical systems, but the domain-dependent content knowledge is expensive to obtain due to the large amount of manual efforts. In this paper, we make use of the large volume of textual reviews for the automatic extraction of domain knowledge, namely, the explicit features aspects in a specific product domain. We thus degrade the product-level modeling of user preferences, which suffers from the lack of data, to the feature-level modeling, which not only grants us the ability to predict user preferences through direct time series analysis, but also allows us to know the essence under the surface of product-level changes in purchasing patterns. Besides, the expanded feature space also helps to make cold-start recommendations for users with few purchasing records. Technically, we develop the Fourier-assisted Auto-Regressive Integrated Moving Average (FARIMA) process to tackle with the year-long seasonal period of purchasing data to achieve daily-aware preference predictions, and we leverage the conditional opportunity models for daily-aware personalized recommendation. Extensive experimental results on real-world cosmetic purchasing data from a major e-commerce website (JD.com) in China verified both the effectiveness and efficiency of our approach. Data integration systems offer a uniform interface to a set of data sources. Despite recent progress, setting up and maintaining a data integration application still requires significant upfront effort of creating a mediated schema and semantic mappings from the data sources to the mediated schema. Many application contexts involving multiple data sources (e.g., the web, personal information management, enterprise intranets) do not require full integration in order to provide useful services, motivating a pay-as-you-go approach to integration. With that approach, a system starts with very few (or inaccurate) semantic mappings and these mappings are improved over time as deemed necessary. This paper describes the first completely self-configuring data integration system. The goal of our work is to investigate how advanced of a starting point we can provide a pay-as-you-go system. Our system is based on the new concept of a probabilistic mediated schema that is automatically created from the data sources. We automatically create probabilistic schema mappings between the sources and the mediated schema. We describe experiments in multiple domains, including 50-800 data sources, and show that our system is able to produce high-quality answers with no human intervention.