A Methodology for Domain-Spanning Change Impact Analysis

Software change impact analysis (CIA) methods enable developers to understand potential impacts of a code change so that the change can be executed confidently without affecting reliability of the software. However, existing CIA approaches do not support CIA for all source code granularities. Additionally, they lack support for inter-granular change impact queries and hidden dependencies. This paper introduces Augur, an automated static code analysis-based CIA approach that addresses these shortcomings. Augur infers and maintains semantic and environment dependencies along with data and control dependencies between source code entities across granularities. Additionally, Augur uses Change Impact Query Language, a novel query language for impact analysis proposed in this paper, to support inter-granular CIA queries with batch querying feature. Augur has been realized as a Visual Studio extension called Augur-Tool. We have conducted quantitative evaluation on two open-source and two industrial projects to assess the accuracy of the tool. Results from the evaluation indicate that Augur provides CIA with high accuracy (average precision 55% and average recall 85%).

FCA–CIA: An approach of using FCA to support cross-level change impact analysis for object oriented Java programs

We propose a technique for analysis of impact of design changes using dynamic slicing of UML models. For a software architecture specified using UML, we first transform a given model into a graph representation which we have named Model Dependency Graph (MDG). MDG combines information available in the interaction model along with the relevant information available in class model into an integrated model. For a given slicing criterion, our slicing algorithm traverses the constructed MDG to identify the relevant model elements forming the dynamic slice. We use these slices to identify the model changes, and their impact. Our impact analysis methodology is based on using the difference between the dynamic slices computed before and after changes are made to the model. Our methodology's novelty lies in performing impact analysis based on a graph metamodel as against independently processing separate UML models.

Enterprise architecture (EA) models the whole enterprise in various aspects regarding both business processes and information technology resources. As the organisation grows, the architecture of its systems and processes must also evolve to meet the demands of the business environment. Evolving an EA model may involve making changes to various components across different levels of the EA. As a result, an important issue before making a change to an EA model is assessing the ripple effect of the change, i.e. change impact analysis. Another critical issue is change propagation: given a set of primary changes that have been made to the EA model, what additional secondary changes are needed to maintain consistency across multiple levels of the EA. There has been however limited work on supporting the maintenance and evolution of EA models. This article proposes an EA description language, namely ChangeAwareHierarchicalEA, integrated with an evolution framework to support both change impact analysis and change propagation within an EA model. The core part of our framework is a technique for computing the impact of a change and a new method for generating interactive repair plans from Alloy consistency rules that constrain the EA model.

Integrated Solution Engineering helps developers manage software complexity by offering semi-automated support for capturing and mining relationships among artifacts and/or developer tasks at different stages of the software lifecycle, and by aiding developers in the use and management of the information contained in these relationships. The use of these relationships can facilitate traceability, propagation of change, change impact analysis, evolution, and comprehension.

Program understanding technologies can be applied effectively in the analysis phase of a software change process. The analysis phase naturally follows a goal driven metaprocess. Described are issues involved with inserting program understanding technology into existing practice and into such a metaprocess. The implied processes of program understanding and reverse engineering tools play an important role. These issues pose major problems for the acceptance of redocumentation tools such as Rigi, an evolvable reverse engineering tool. An example using Rigi and its analysis methodology for change impact analysis is considered.

Software-as-a-service multi-tenancy helps service providers to cut cost, improve resource utilization, and reduce service customization and maintenance time as the tenants share the same service instance. However, existing large-scale business applications inherently do not support multi-tenancy. This hinders these applications' vendors from adopting the cloud model. Thus reengineering such applications to support multi-tenancy has become a key requirement. Reengineering such applications to support multi-tenancy is a complex and challenging task as it requires a deep understanding of the given application and almost all system modules need to be revisited. In this paper we introduce SMURF, Supporting Multi-tenancy Using Reengineering Aspects 'Re-Aspect' Framework, to help service providers reengineering their legacy applications to support multi-tenancy. SMURF is based on our new re-aspects concept where a given system modification to disable, modify, replace or inject code is encapsulated in a re-aspect. SMURF realizes given system modifications (captured as re-aspects) by automating both change impact analysis and change propagation processes. We analyze the SaaS multi-tenancy pattern, discuss the set of requirements to migrate a single-tenant application to support multi-tenancy, describe SMURF approach, architecture and implementation details, and experimental results of SMURF given a set of open source web applications.

In this paper, we propose a formal model and a platform for software change management. The model is based on graphs rewriting, and deals with both multi-language source codes and heterogeneous database schemas. These are represented by software components linked by meaningful relationships. The change impact analysis is done, using a knowledge-based system, that includes impact propagation rules preserving the software consistency. This is implemented by an integrated platform including a multilanguage parsing tool, and a soft-ware change management module.

Impact of dynamic and static fast inductive charging of electric vehicles on the distribution network

Change management is a critical task to control the side effects of a modification during the business process evolution. The evolution of business processes is an essential activity for the companies to better fulfill the requirements of their customers and different stakeholders. In this respect, the enterprises should adopt an effective mechanism in order to achieve the flexible business process models. It is important to identify and highlight the ripple effects of a change for minimizing their impact on other parts or entities of the system and associated services. This paper proposes a dependency-centric approach for change impact analysis. We attempt to demonstrate the change impact propagation in business process models by detecting and analyzing the interdependencies among all parts of business processes along with associated services. It can support the maintenance and evolution of business process models. The major objective is to help the modelers and business experts to assess the associated risk of intended changes and estimate the effort required for their accomplishments.

Economic Value and Environmental Impact analysis tool for sustainable biorefinery design

Insufficient consideration of failures caused by the product while interacting with its environment during the use-phase can lead to critical accidents as the well-known Tesla car accident has made it clear, where the autopilot system of the car was unable to differentiate between the background of the bright sky and the white side of the truck causing a violent crash and the death of the driver. This demonstrates that failures caused by the interaction of the product and its environment do not only affect its quality but also - in the worst case - can lead to human injuries or even death. Even though there are already several methods for failure analysis in the product development process, e.g., FMEA, FTA, Ishikawa, none of them sufficiently consider the interaction between the product and its environment. It is, therefore, necessary to develop a new methodology that can systematically identify potential failures resulting from these interactions. As support for the methodology, it is also important to develop an enhanced system model relying on the fundamental principles of Systems Engineering.This paper introduces a new methodology for a model-based failure analysis that examines the interaction between a technical product and its environment and identifies its potential failures during the use-phase. The methodology is evaluated using a case study from the electric industry (linear induction motor).

BACKGROUND Patient similarity is a fundamental concept in precision oncology, offering a pathway to personalized medicine by identifying patterns and shared characteristics among patients. This concept enables stratification into clinically meaningful subgroups, prediction of treatment responses, and the tailoring of therapeutic interventions to individual needs. Despite its transformative potential, the definition, measurement, and clinical application of patient similarity remains inconsistently established, creating challenges in its integration into cancer research and clinical practice. OBJECTIVE The goal of this scoping review is to synthesize evidence on the multidimensional concept of patient similarity in cancer research by analyzing its application across different points of possible data types, methodological frameworks, biological contexts, and commonly studied cancer types. METHODS This scoping review followed the PRISMA-ScR framework and the Joanna Briggs Institute guidelines. A systematic search was conducted across PubMed, MEDLINE, LIVIVO, and Web of Science (1998-February 2024), supplemented by snowball sampling and manual searches. Duplicate records were removed, and study selection was carried out in three phases: title and abstract screening, disagreement resolution, and full-text screening. Each step was independently performed by two reviewers in Rayyan, with conflicts resolved by a third reviewer. Data extraction was performed using a predefined template to capture methodological approaches, data types, cancer types, and research objectives related to cancer patient similarity. RESULTS This scoping review synthesized evidence from 137 studies, emphasizing the multidimensional concept of patient similarity in cancer research, which integrates diverse data types, methodological frameworks, research objectives, and cancer types. Transcriptomic data (67.1%, 92/137) and clinical data (47.4%, 65/137) were the most frequently used, often combined to enhance the comprehensiveness of similarity analyses. Machine learning (55.5%, 76/137) and network-based approaches (52.5%, 72/137) were prominent methods, reflecting their capacity to handle complex, high-dimensional data and uncover intricate relationships. Cancer subtype identification (51.1%, 70/137) and biomarker discovery (29.9%, 41/137) were the primary research objectives, underscoring the centrality of patient similarity in precision oncology. Breast, lung, and brain cancers were the most frequently studied, benefiting from established research frameworks and abundant datasets. Conversely, rare cancers were underrepresented, highlighting a critical gap in the generalizability of current methodologies. CONCLUSIONS This comprehensive scoping review examines the concept of patient similarity in cancer research and highlights the critical role of a multi-layered perspective in capturing its complexity and identification to enhance understanding and application in precision oncology.

An automated change impact analysis and management approach is vital to handle the complexity of adapting software during its evolution. Such an approach reduces the maintenance cost and provides for adequate decision making when confronted with the choice of accepting or ignoring changes. This paper presents a change impact management approach between UML models. It verifies the consistency and the quality of interdependent diagrams after a change is handled. In addition, it calculates the effort required in managing any change and displays a report indicating to the designer all necessary modifications to keep the design coherent. The approach is supported by a toolset, called CQV-UML tool.

The reality that climate change inevitably affects our world at present is taken into consideration and adaptation to climate change studies are becoming more pronounced at international scale. Although there are some studies related to the determination of adaptation measures in our country and in the world, there is still no common methodology for adaptation on a measurable basis. In this study, a new approach developed under the “Effects of Climate Change on Water Resources Project” was used to execute a sectorial vulnerability analysis. Sectorial vulnerability analyses have great importance in terms of determining the most vulnerable sector to climate change and of the related policy. In this study, the methodology of sectorial vulnerability analysis is explained in water intended for human consumption sector for 2060s and at the scale of Büyük Menderes River Basin. The results showed that the impact level was expected as “medium” for the water intended for human consumption sector.