Technical Debt in Data-Intensive Software Systems

Foidl, Harald; Felderer, Michael; Biffl, Stefan

doi:10.1109/seaa.2019.00058

Cited by 21 publications

(13 citation statements)

References 18 publications

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“…Foidl et al claim that technical debt can be incurred in different parts (i.e., software systems, data storage systems, data) of data-intensive systems and different parts can further affect each other [12]. They propose a conceptual model to outline where technical debt can emerge in data-intensive systems by separating them into three parts: software systems, data storage systems and data.…”

Section: Technical Debt In Data-intensive Systemsmentioning

confidence: 99%

“…They often have critical roles, too, calling for prime importance to ensure their quality. Data-intensive systems, however, have several peculiarities posing challenges to software engineering practitioners and researchers [10,12,17,35].…”

Section: Introductionmentioning

confidence: 99%

“…Technical debt is pertinent to data-intensive systems too. In a recent study, Foidl et al claim that technical debt can proliferate in data-intensive systems [12]. As they say, data-intensive systems have heterogeneous architecture divided into multiple parts (software systems, data storage systems, and data), and debt introduced in one part has unwanted effects on other parts as well.…”

Section: Introductionmentioning

confidence: 99%

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FIXME: Synchronize with Database An Empirical Study of Data Access Self-Admitted Technical Debt

Muse¹,

Nagy²,

Cleve³

et al. 2022

Preprint

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Developers sometimes choose design and implementation shortcuts due to the pressure from tight release schedules. However, shortcuts introduce technical debt that increases as the software evolves. The debt needs to be repaid as fast as possible to minimize its impact on software development and software quality. Sometimes, technical debt is admitted by developers in comments and commit messages. Such debt is known as self-admitted technical debt (SATD). In data-intensive systems, where data manipulation is a critical functionality, the presence of SATD in the data access logic could seriously harm performance and maintainability. Understanding the composition and distribution of the SATDs across software systems and their evolution could provide insights into managing technical debt efficiently. We present a large-scale empirical study on the prevalence, composition, and evolution of SATD in data-intensive systems. We analyzed 83 open-source systems relying on relational databases as well as 19 systems relying on NoSQL databases. We detected SATD in source code comments obtained from different snapshots of the subject systems. To understand the evolution dynamics of SATDs, we conducted a survival analysis. Next, we performed a manual analysis of 361 sample data-access SATDs, investigating the composition of data-access SATDs and the reasons behind their introduction and removal. We identified 15 new SATD categories, out of which 11 are specific to database access operations. We found that most of the data-access SATDs are introduced in the later stages of change history rather than at the beginning. We also observed that bug fixing and refactoring are the main reasons behind the introduction of data-access SATDs.

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Section: Technical Debt In Data-intensive Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FIXME: Synchronize with Database An Empirical Study of Data Access Self-Admitted Technical Debt

Muse¹,

Nagy²,

Cleve³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, we found two studies about technical debt and the design stage, for which we created the category design debt [4,58], and resources for teaching software design for AI-based systems [118].…”

Section: Software Design (34 Studies)mentioning

confidence: 99%

Software Engineering for AI-Based Systems: A Survey

Martínez-Fernández,

Bogner,

Franch

et al. 2021

Preprint

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AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-and speechrecognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems.To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula. CCS Concepts: • Software and its engineering → Software creation and management; • Computing methodologies → Machine learning;

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“…[63] Attempts at a framework for how to measure and monitor technical debt remain theoretical at best [64][65][66][67][68][69] . Estimates hold that in the development of machine learning systems, technical debt accrues at a rate comparable to that of a high-interest credit card [70,71] . Researchers at Google, Inc. warn of compounding "correction cascades" in these fragile models, meaning hidden feedback loops, signal entanglements, and other technical challenges due to what they describe as the CACE principle, for "Changing Anything Changes Everything" [70] .…”

Section: Technical Debtmentioning

confidence: 99%

Algorithmic Silence: A Call to Decomputerize

Penn

2021

J. Soc. Comput.

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Tech critics become technocrats when they overlook the daunting administrative density of a digital-first society. The author implores critics to reject structural dependencies on digital tools rather than naturalize their integration through critique and reform. At stake is the degree to which citizens must defer to unelected experts to navigate such density. Democracy dies in the darkness of sysadmin. The argument and a candidate solution proceed as follows. Since entropy is intrinsic to all physical systems, including digital systems, perfect automation is a fiction. Concealing this fiction, however, are five historical forces usually treated in isolation: ghost work, technical debt, intellectual debt, the labor of algorithmic critique, and various types of participatory labor. The author connects these topics to emphasize the systemic impositions of digital decision tools, which compound entangled genealogies of oppression and temporal attrition. In search of a harmonious balance between the use of "AI" tools and the non-digital decision systems they are meant to supplant, the author draws inspiration from an unexpected source: musical notation. Just as musical notes require silence to be operative, the author positions algorithmic silence-the deliberate exclusion of highly abstract digital decision systems from human decision-making environments-as a strategic corrective to the fiction of total automation. Facial recognition bans and the Right to Disconnect are recent examples of algorithmic silence as an active trend.

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Technical Debt in Data-Intensive Software Systems

Cited by 21 publications

References 18 publications

FIXME: Synchronize with Database An Empirical Study of Data Access Self-Admitted Technical Debt

FIXME: Synchronize with Database An Empirical Study of Data Access Self-Admitted Technical Debt

Software Engineering for AI-Based Systems: A Survey

Algorithmic Silence: A Call to Decomputerize

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