Investigating Technical Debt Folklore - Shedding some light on technical debt opinion

We identified and organized a number of statements about technical debt (TD Folklore list) expressed by practitioners in online websites, blogs and published papers. We chose 14 statements and we evaluated them through two surveys (37 practitioners answered the questionnaires), ranking them by agreement and consensus. The statements most agreed with show that TD is an important factor in software project management and not simply another term for "bad code". This study will help the research community in identifying folklore that can be translated into research questions to be investigated, thus targeting attempts to provide a scientific basis for TD management

Investigating Technical Debt Folklore - Shedding some light on technical debt opinion

We identified and organized a number of statements about technical debt (TD Folklore list) expressed by practitioners in online websites, blogs and published papers. We chose 14 statements and we evaluated them through two surveys (37 practitioners answered the questionnaires), ranking them by agreement and consensus. The statements most agreed with show that TD is an important factor in software project management and not simply another term for “bad code”. This study will help the research community in identifying folklore that can be translated into research questions to be investigated, thus targeting attempts to provide a scientific basis for TD management

Factors affecting Technical Debt Raw data from a systematic literature map

"This document presents the complete list of references that have been short listed during the systematic review process carried out during the months of April-September 2012. The objective of the systematic review was to identify current research trends in technical debt and to explore the relationship between technical debt measures and agile software development. This documents includes 352 references that are categorized according to their relevance to technical debt research." [Abstract

Un programa de investigación en deuda técnica de software

"La ingeniería de software es la disciplina del conocimiento que se ocupa del problema de producir software. El software es cada día más pervasivo en nuestra vida. Sin embargo, la ingeniería de software es una disciplina reciente y todavía basada en modas y referentes. Las teorías comprensivas y la investigación rigurosa con respecto a la producción de software son escasas. La ingeniería de software empírica tiene por objetivo producir conocimiento confiable y aplicable a la producción de software. Esta área de investigación aplica el método científico experimental en la investigación en ingeniería de software. En este taller se presentan mecanismos de investigación utilizados por la comunidad de investigación en Ingeniería de software empírica. Entre los métodos presentado se discuten métodos primarios de investigación (como experimentos y casos de estudio) y métodos secundarios (revisiones sistemáticas de la literatura). Para ejemplificar la aplicación de esta visión de la investigación, este taller presenta como se han aplicado estos conceptos para conducir un programa de investigación en deuda técnica." [Abstract

Discovering and Assessing Enterprise Architecture Debts

The term Enterprise Architecture (EA) Debts has been coined to grasp the difference between the actual state of the EA and its hypothetical, optimal state. So far, different methods have been proposed to identify such EA Debts in organizations. However, these methods either are based on the transfer of known concepts from other domains to EA or are time and resource intensive. To overcome these shortcomings, we propose an approach that uses an interview format to identify EA Debts in enterprises and a method that allows a qualitative assessment of identified EA Debts. The proposed approach is supported by the designed framework that consists of an interview format and a process for determining thresholds of certain EA Smells

An Empirical Study of Self-Admitted Technical Debt in Machine Learning Software

The emergence of open-source ML libraries such as TensorFlow and Google Auto ML has enabled developers to harness state-of-the-art ML algorithms with minimal overhead. However, during this accelerated ML development process, said developers may often make sub-optimal design and implementation decisions, leading to the introduction of technical debt that, if not addressed promptly, can have a significant impact on the quality of the ML-based software. Developers frequently acknowledge these sub-optimal design and development choices through code comments during software development. These comments, which often highlight areas requiring additional work or refinement in the future, are known as self-admitted technical debt (SATD). This paper aims to investigate SATD in ML code by analyzing 318 open-source ML projects across five domains, along with 318 non-ML projects. We detected SATD in source code comments throughout the different project snapshots, conducted a manual analysis of the identified SATD sample to comprehend the nature of technical debt in the ML code, and performed a survival analysis of the SATD to understand the evolution of such debts. We observed: i) Machine learning projects have a median percentage of SATD that is twice the median percentage of SATD in non-machine learning projects. ii) ML pipeline components for data preprocessing and model generation logic are more susceptible to debt than model validation and deployment components. iii) SATDs appear in ML projects earlier in the development process compared to non-ML projects. iv) Long-lasting SATDs are typically introduced during extensive code changes that span multiple files exhibiting low complexity