Managing Technical Debt in Database Schemas of Critical Software

Weber, Jens H.; Cleve, Anthony; Meurice, Loup; Ruiz, Francisco

doi:10.1109/mtd.2014.17

Cited by 20 publications

(11 citation statements)

References 4 publications

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“…But as the number of papers is still small, and mostly the strategies have not been evaluated, it is not possible to say whether the two strategies are more effective than other strategies. 3 Calculation of TD-Principal [ Curtis et al, 2012b] 2 Marking of dependencies and Code Issues [Morgenthaler et al, 2012] [ Tom et al, 2013] 2 Debt Symptoms Index [Marinescu, 2012] 1 Empirical Model of Technical Debt and Interest [Nugroho et al, 2011] 1 Formal Approach to Technical Debt Decision Making 1 Game Theoretic Competitive Source Control Approach [Morrison-Smith et al, 2012] 1 Measuring symptom severity on a smell thermometer [Ligu et al, 2013] 1 Metric for Managing Architectural Technical Debt 1 RE-KOMBINE Model [Ernst, 2012] 1 SQALE Method [Letouzey and Ilkiewicz, 2012] 1 Supply Chain Management 1 Framework for Estimating Interest [Singh et al, 2014] 1 Managing TD in database schemas [Weber et al, 2014] 1…”

Section: )mentioning

confidence: 99%

Identification and management of technical debt: A systematic mapping study

Alves

Mendes

Mendonça

et al. 2016

Information and Software Technology

262

153

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Context: The technical debt metaphor describes the effect of immature artifacts on software maintenance that bring a short-term benefit to the project in terms of increased productivity and lower cost, but that may have to be paid off with interest later. Much research has been performed to propose mechanisms to identify debt and decide the most appropriate moment to pay it off. It is important to investigate the current state of the art in order to provide both researchers and practitioners with information that enables further research activities as well as technical debt management in practice. Objective: This paper has the following goals: to characterize the types of technical debt, identify indicators that can be used to find technical debt, identify management strategies, understand the maturity level of each proposal, and identify what visualization techniques have been proposed to support technical debt identification and management activities. Method: A systematic mapping study was performed based on a set of three research questions. In total, 100 studies, dated from 2010 to 2014, were evaluated. Results: We proposed an initial taxonomy of technical debt types, created a list of indicators that have been proposed to identify technical debt, identified the existing management strategies, and analyzed the current state of art on technical debt, identifying topics where new research efforts can be invested. Conclusion: The results of this mapping study can help to identify points that still require further investigation in technical debt research

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Section: )mentioning

confidence: 99%

Identification and management of technical debt: A systematic mapping study

Alves

Mendes

Mendonça

et al. 2016

Information and Software Technology

262

153

View full text Add to dashboard Cite

show abstract

“…The approach and tools require both databases to be identical apart from the data. Changing the database version, schema (e.g., to modernise its design), or database type (e.g., relational to NoSQL) are significant independent challenges [19][20][21] and outside the scope of our work. We expect many organisations to perform separate projects for cloud database migration and data conversion.…”

Section: Approach Overviewmentioning

confidence: 99%

Evaluating cloud database migration options using workload models

2018

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A key challenge in porting enterprise software systems to the cloud is the migration of their database. Choosing a cloud provider and service option (e.g., a database-as-a-service or a manually configured set of virtual machines) typically requires the estimation of the cost and migration duration for each considered option. Many organisations also require this information for budgeting and planning purposes. Existing cloud migration research focuses on the software components, and therefore does not address this need. We introduce a two-stage approach which accurately estimates the migration cost, migration duration and cloud running costs of relational databases. The first stage of our approach obtains workload and structure models of the database to be migrated from database logs and the database schema. The second stage performs a discrete-event simulation using these models to obtain the cost and duration estimates. We implemented software tools that automate both stages of our approach. An extensive evaluation compares the estimates from our approach against results from real-world cloud database migrations.

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“…Despite the vast contribution on technical debt types, database design debt received very little attention. Weber et al [30] discussed a specific type of schema design debt in missing foreign keys in relational databases. To illustrate the concept, the authors examined this type of debt in an electronic medical record system called OSCAR.…”

Section: Related Workmentioning

confidence: 99%

Identifying Technical Debt in Database Normalization Using Association Rule Mining

Albarak

Al-Razgan

Bahsoon

2018

2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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Technical debt is a metaphor that describes the long-term effects of shortcuts taken in software development activities to achieve near-term goals. In this study, we explore a new context of technical debt that relates to database normalization design decisions. We posit that ill-normalized databases can have longterm ramifications on data quality and maintainability costs over time, just like debts accumulate interest. Conversely, conventional database approaches would suggest normalizing weakly normalized tables; this can be a costly process in terms of effort and expertise it requires for large software systems. As studies have shown that the fourth normal form is often regarded as the "ideal" form in database design, we claim that database normalization debts are likely to be incurred for tables below this form. We refer to normalization debt item as any table in the database below the fourth normal form.We propose a framework for identifying normalization debt. Our framework makes use of association rule mining to discover functional dependencies between attributes in a table, which will help determine the current normal form of that table and identify debt tables. To manage such debts, we propose a trade-off analysis method to prioritize tables that are candidate for normalization. The trade-off is between the rework cost and the debt effect on the data quality and maintainability as the metaphoric interest. To evaluate our method, we use a case study from Microsoft, AdventureWorks. The results show that our method can reduce the cost and effort of normalization, while improving the database design. CCS CONCEPTS• Software and its engineering → Designing software;• Information systems → Relational Database Model;

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Managing Technical Debt in Database Schemas of Critical Software

Cited by 20 publications

References 4 publications

Identification and management of technical debt: A systematic mapping study

Identification and management of technical debt: A systematic mapping study

Evaluating cloud database migration options using workload models

Identifying Technical Debt in Database Normalization Using Association Rule Mining

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