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Financial Business Intelligence System (fbis)

Functional papers represent the most advanced research in the field with the greatest potential for impact. A feature paper should be a seminal original article that incorporates several techniques or approaches, provides insight into future research directions, and describes possible research applications.

Pdf) Cognitive Hacking And Intelligence And Security Informatics

Functional papers are submitted by personal invitation or recommendation of scientific editors and must receive positive feedback from reviewers.

Editor’s Choice articles are based on recommendations from scientific editors of journals around the world. The editors select a small number of recently published articles in the journal that they believe are of particular interest to readers, or are important in the relevant research area. The aim is to provide a snapshot of the most interesting work published in the various research areas of the journal.

Technical Debt Priority in Telecom Applications: Why Actual Refactoring Goes Off Plan and How to Eliminate It? Case in point during the COVID era

Marek G. Stochel Marek G. Student Publications 2

Pdf] Research On Using Anp To Establish A Performance Assessment Model For Business Intelligence Systems

Submission received: September 30, 2022 / Updated: November 2, 2022 / Accepted: November 4, 2022 / Published: November 8, 2022

This paper focuses on the application of technical task prioritization techniques in telecommunications software that manages a fleet of devices for a video surveillance system. The technical debt for this application was collected, categorized and prioritized according to the debt assessment approach (CoDVA) proposed by the authors. The following research questions were asked: Is prioritizing technical debt reduction under CoDVA effective (ie, is it being implemented as planned, delivering tangible benefits)? The spread of the COVID-19 pandemic has created unprecedented disruption in engineering organizations around the world, so the technical task identification phase had to be adapted to the transition to a mandatory work-from-home mode. This was achieved by using a crowd-sourced approach, ensuring broad engineering involvement, and providing a fairly comprehensive picture of the accumulated technical commitment. Nevertheless, measures to reduce technical liabilities did not meet the expected guidelines. Three main reasons for this phenomenon have been identified: continuous refactoring approach, increasing technical debt items, and expanding refactoring activities. Therefore, as a result of this case study, we propose to adopt an expanded definition of technical obligation and follow several rules for determining its scope and granularity.

Experience with COVID software; software engineering; software engineering and commitment metaphors; software development and evolution; technical debt; technical debt management; technical debt advantage; The wisdom of the people

The evolution of telecommunication software, technologies, tools, and business models in a changing landscape leads to the emergence of a large number of technical tasks [1]. However, its understanding has evolved over time, and a widely used definition is as follows: “In software-intensive systems, a technical task consists of constructing, designing, or implementing designs that are purposeful in a short-term but technical context. future change is more expensive or impossible. Technical commitment is a conditional responsibility, the influence of which is limited to internal system qualities – primarily, not only stability and evolution” [2]. Additionally, technical debt includes a product’s technical debt items (TD items, TDI), which represent immature artifacts and deviations from the desired optimal state. In our previous paper, we argued that the definition of technical responsibility should be expanded to include all software responsible for delivering a product to the customer as the business perspective shifts to a service model rather than a one-off sale. In addition, even with a one-time sale, the seller is responsible for providing agreed-upon service for a predetermined period of time (linked to a Service Level Agreement). This means that what makes up a stable development environment is testing capability (automation), code analysis tools, continuous integration and delivery, etc. All software artifacts provided by the provider may be subject to technical commitment dynamics. Therefore, they should be part of a telecommunications software solution that becomes a service offered to customers. Moreover, in such a broad context, properly targeted improvements can improve the overall solution: its stability, testability, ability to deliver updates quickly; additionally improving the predictability of the development team [3].

Enhancing Supply Chain Risk Management By Applying Machine Learning To Identify Risks

The spread of the coronavirus pandemic (COVID-19) has caused unprecedented disruption in personal life, the global economy, and the business processes of organizations. The field of software engineering was not affected by it, and in particular the initial design of the industrial case studies discussed in this article had to be modified to take into account the domestic mandatory policies. This paper presents the preliminary results of more than a year of research, and describes the application of a technical task prioritization technique to a product that manages a fleet of connected devices for a video surveillance system. We decided to use the modified Continuous Debt Valuation Approach (CoDVA) technique for debt assessment and prioritization, which we have previously developed [4]. Furthermore, a prerequisite for the successful application of the CoDVA method is the step of identifying the technical challenge; in our case, it was performed based on the Wisdom approach [5], which has proven to be effective in other experiments [6].

Finally, we asked the research question: Is prioritizing the reduction of technical liabilities under CoDVA effective (ie delivered as planned, delivering tangible benefits)? Several interesting observations were made in our case. The implementation of technical debt reduction did not meet the expected guidelines. We have identified three main reasons for this: implementation of a continuous refactoring method, efforts to reduce the required technical tasks (planned refactoring), and additional necessary activities that are not considered part of the technical task reduction. Now, after reviewing the results, we propose to adopt a specific definition of technical debt and follow several rules for determining the amount of technical debt reduction, prioritizing it, and performing reactive actions to improve the overall program quality and stability. Preliminary results from these industrial cases have proven that product quality has improved over time, as a downward trend in stability issues has been observed.

In summary, this paper provides more detailed information on the conceptualization and practical application of our technical task assessment and prioritization (CoDVA) technique. Observations made during the research show how to mitigate the potential risks associated with the functional use of the concept. Additionally, this method proves that it can be carried out during the forced work mode imposed on organizations during the COVID-19 pandemic, and despite the difficulties, the development team can refer to important reactors (reduction of technical tasks) that have been significantly achieved. results.

Paper construction. The remainder of the paper covers the following topics: Section 2 provides background and related work. Chapter 3 presents the methodology, including data collection and analysis processes. A discussion of the findings is presented in Section 4, followed by an analysis of potential threats to the validity of the study in Section 5. Finally, conclusions and future work are presented in Section 6.

Loch K. Johnson) Strategic Intelligence

The term technical debt is widely used; however, due to its metaphorical origin, it gradually became ambiguous [7]. Therefore, the operational definition used in our work is presented in Section 1 along with additional comments. In addition, the term technical task also refers to the set of all technical tasks related to the system [2]. A technical commitment point (TDI) represents an immature software development artefact that represents the gap between the current and desired state [4]. The act of reducing technical debt is called refactoring [8] or debugging. The evolution of software development and delivery techniques, namely the adoption of the DevOps paradigm and its continuous integration and continuous delivery (CI/CD) practices, requires a technical engagement perspective. Consequently, a wider pool of software development artifacts will be considered part of a telecommunications system or service as more software artefacts experience technical commitment dynamics. In fact, by observing the trends of stability indicators, we can estimate which parts of the codebase are likely to be abandoned by developers and which cannot be pre-written [10]. Developers themselves can clearly indicate the introduction of technical debt (eg, self-confessed technical debt), and its removal often takes the form of rewriting the affected code rather than correction [11]. However, we must avoid the trap of understanding the technical task at the scale offered by tools such as SonarQube [12]. Our understanding must revolve around the gap between the current and desired state of the artifacts that make up the system or service.

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