Change Impact Analysis for Evolving Configuration Decisions in Product Line Use Case Models

Product Line Engineering is becoming a key practice in many software development environments where complex systems are developed for multiple customers with varying needs. In many business contexts, use cases are the main artifacts for communicating requirements among stakeholders. In such contexts, Product Line (PL) use cases capture variable and common requirements while use case-driven configuration generates Product Specific (PS) use cases for each new customer in a product family. In this paper, we propose, apply, and assess a change impact analysis approach for evolving configuration decisions in PL use case models. Our approach includes: (1) automated support to identify the impact of decision changes on prior and subsequent decisions in PL use case diagrams and (2) automated incremental regeneration of PS use case models from PL use case models and evolving configuration decisions. Our tool support is integrated with IBM Doors. Our approach has been evaluated in an industrial case study, which provides evidence that it is practical and beneficial to analyze the impact of decision changes and to incrementally regenerate PS use case models in industrial settings

A Systematic Review of Tracing Solutions in Software Product Lines

Software Product Lines are large-scale, multi-unit systems that enable massive, customized production. They consist of a base of reusable artifacts and points of variation that provide the system with flexibility, allowing generating customized products. However, maintaining a system with such complexity and flexibility could be error prone and time consuming. Indeed, any modification (addition, deletion or update) at the level of a product or an artifact would impact other elements. It would therefore be interesting to adopt an efficient and organized traceability solution to maintain the Software Product Line. Still, traceability is not systematically implemented. It is usually set up for specific constraints (e.g. certification requirements), but abandoned in other situations. In order to draw a picture of the actual conditions of traceability solutions in Software Product Lines context, we decided to address a literature review. This review as well as its findings is detailed in the present article.Comment: 22 pages, 9 figures, 7 table

Data Mining Implementation to Predict Sales Using Time Series Method

Sales transaction data histories can be used to predict the possibility of sales transaction that will occur in the future. These characteristics are in accordance with forecasting using time series method where this method uses previous data as tools to predict transaction value that will appear in the present time. Company X that runs its business by sell their product through distributors has sales data that is not optimally utilized. The average number of sales per year ranges from 5000 transactions which is not use to forecast transactions hereafter. Transaction data is stored in the company database so that data mining technology can be applied to support company X transaction data collection from previous year. The data is processed in applications where the results of forecasting are compared with real data in 2018 to see the accuracy of the forecasting results. The graphic that shown in application has pattern which can use for forecasting. From the forecasting method used, it can be seen that the forecasting results show data that came out did not produce data that matched the real data where the highest level of accuracy was 99.68% and the lowest accuracy was still above 50%