Demo Data from VLDB 2026 / reviewers in the wild / expert
Vittorio Marengo
dblp:80/6564
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2Artificial intelligence and machine learning · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Software maintenance and evolution · 91% Requirements engineering and software design · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software maintenance and evolution › software reengineering
legacy system reengineering |
0.0 | 1 | 2003 | Iterative Reengineering of Legacy Systems · IEEE Trans. Software Eng. 2003 |
Software maintenance and evolution › software evolution
software aging |
0.0 | 1 | 2003 | Iterative Reengineering of Legacy Systems · IEEE Trans. Software Eng. 2003 |
Software maintenance and evolution
software reengineering |
0.0 | 1 | 2003 | Iterative Reengineering of Legacy Systems · IEEE Trans. Software Eng. 2003 |
Requirements engineering and software design
software architecture |
0.0 | 1 | 2003 | Iterative Reengineering of Legacy Systems · IEEE Trans. Software Eng. 2003 |
Methods — techniques the papers use, named apart from their topics
iterative process model · 0.0case study · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Iterative Reengineering of Legacy SystemsabstractDuring its life, a legacy system is subjected to many maintenance activities, which cause degradation of the quality of the system: When this degradation exceeds a critical threshold, the legacy system needs to be reengineered. In order to preserve the asset represented by the legacy system, the familiarity with it gained by the system's maintainers and users, and the continuity of execution of current operations during the reengineering process, the system needs to be reengineered gradually. Moreover, each program needs to be reengineered within a short period of time. The paper proposes a reengineering process model, which is applied to an in-use legacy system to confirm that the process satisfies previous requirements and to measure its effectiveness. The reengineered system replaced the legacy one to the satisfaction of all the stakeholders; the reengineering process also had a satisfactory impact on the quality of the system. Finally, this paper contributes to validate the cause-effect relationship between the reengineering process and overcoming the aging symptoms of a software system. Alessandro Bianchi, Danilo Caivano, Vittorio Marengo, Giuseppe Visaggio |
IEEE Trans. Software Eng. | 3 |
| 2001 | Iterative Reengineering of Legacy FunctionsabstractThis paper describes a process of gradual reengineering of the procedural components of a legacy system. The process is integrated and completed by the data reengineering process analyzed in a previous paper by the same authors. The proposed method enables the legacy system to be gradually emptied into the reengineered system, without needing to either duplicate the legacy system or freeze it. The process consists of evolving the legacy system components toward firstly a restored system and then toward the reengineered system. Meanwhile, the legacy system can coexist with both the restored and the reengineered parts. By the end of the process, a single system will be in existence: the reengineered one. The method has been applied to reengineer a real system and demonstrated its ability to: support gradual reengineering, maintain the system at work during the process, minimize the need to freeze maintenance requests, renew the operative environment of the reengineered system with respect to the legacy system and, finally, eliminate all the system's aging symptoms. Alessandro Bianchi, Danilo Caivano, Vittorio Marengo, Giuseppe Visaggio |
ICSM | 3 |
| 2001 | Inductive learning from numerical and symbolic data: An integrated framework
Floriana Esposito, Donato Malerba, Vittorio Marengo |
Intell. Data Anal. | 3 |