Related papers: Towards a Technical Debt for Recommender System

Technical debt denotes shortcuts taken during software development, mostly for the sake of expedience. When such shortcuts are admitted explicitly by developers (e.g., writing a TODO/Fixme comment), they are termed as Self-Admitted…

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing…

The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a…

Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data. This paper presents a…

Recommender systems play a pivotal role in helping users navigate an overwhelming selection of products and services. On online platforms, users have the opportunity to share feedback in various modes, including numerical ratings, textual…

Recommender systems are nowadays a pervasive part of our online user experience, where they either serve as information filters or provide us with suggestions for additionally relevant content. These systems thereby influence which…

Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating…

Technical debt occurs in many different forms across software artifacts. One such form is connected to software architectures where debt emerges in the form of structural anti-patterns across architecture elements, namely, architecture…

In this big data era, it is hard for the current generation to find the right data from the huge amount of data contained within online platforms. In such a situation, there is a need for an information filtering system that might help them…

As recommender systems become widely deployed in different domains, they increasingly influence their users' beliefs and preferences. Auditing recommender systems is crucial as it not only ensures the continuous improvement of…

As software systems continue to play a significant role in modern society, ensuring their fairness has become a critical concern in software engineering. Motivated by this scenario, this paper focused on exploring the multifaceted nature of…

Generative AI is accelerating software development, but may quietly shift where the most significant risks lie. As AI generates code faster than teams can understand it, two under appreciated forms of debt accumulate: cognitive debt, the…

Self-Admitted Technical Debt (SATD) is a form of Technical Debt where developers document the debt using source code comments (SATD-C) or issues (SATD-I). However, it is still unclear the circumstances that drive developers to choose one or…

Recommender systems are the algorithms which select, filter, and personalize content across many of the worlds largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively…

Objective. In this work, we report the experience of a Finnish SME in managing Technical Debt (TD), investigating the most common types of TD they faced in the past, their causes, and their effects. Method. We set up a focus group in the…

Many software developments projects fail due to quality problems. Software testing enables the creation of high quality software products. Since it is a cumbersome and expensive task, and often hard to manage, both its technical background…

Motivation: Technical debt is a metaphor that describes not-quite-right code introduced for short-term needs. Developers are aware of it and admit it in source code comments, which is called Self- Admitted Technical Debt (SATD). Therefore,…

The use of mobile devices in combination with the rapid growth of the internet has generated an information overload problem. Recommender systems is a necessity to decide which of the data are relevant to the user. However in mobile devices…

Recommender systems can be formulated as a matrix completion problem, predicting ratings from user and item parameter vectors. Optimizing these parameters by subsampling data becomes difficult as the number of users and items grows. We…

Self-admitted technical debt (SATD) is a particular case of Technical Debt (TD) where developers explicitly acknowledge their sub-optimal implementation decisions. Previous studies mine SATD by searching for specific TD-related terms in…