BACKGROUND: Understanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their spatial coordinates. RESULTS: We present SC2Spa, a deep learning-based approach that learns intricate spatial relationships from spatial transcriptomics (ST) data. Benchmarking tests show that SC2Spa outperformed other predictors and accurately detected tissue architecture from transcriptome. SC2Spa successfully mapped single cell RNA sequencing (scRNA-seq) to Visium assay, providing an approach to enhance the resolution for low resolution ST data. Our test showed that SC2Spa performs well for various ST technologies and robust to spatial resolution. In addition, SC2Spa can suggest spatially variable genes that cannot be identified from previous approaches. CONCLUSIONS: SC2Spa is a robust and accurate approach to provide single cells with their spatial location and identify spatially meaningful genes.

Many web applications today use responsive design to adjust the view of web pages to match the screen size of end users. People with disabilities often use an alternative view either due to zooming on a desktop device to enlarge text or viewing within a smaller viewport when using assistive technologies. When web pages are not implemented to correctly adjust the page's content across different screen sizes, it can lead to both a loss of content and functionalities between the different versions. Recent studies show that these reflow accessibility issues are among the most prevalent modern web accessibility issues. In this paper, we present a novel automated technique to automatically detect reflow accessibility issues in web pages for keyboard users. The evaluation of our approach on real-world web pages demonstrated its effectiveness in detecting reflow accessibility issues, outperforming state-of-the-art techniques.

Abstract Technical debt (TD) is a metaphor used to refer to the added software system costs acquired from taking shortcuts. Unfortunately, large amounts of TD can lead to serious consequences, and, thus, the management of TD is essential. Due to TD being a relatively new subject of study, many aspects of TD remain ambiguous. Fortunately, Twitter has been proven to hold a wealth of information on many subjects. As such, this survey study aims to gain a better understanding on how interest in TD has evolved over time and how TD is addressed on Twitter. A total of 128,897 TD‐related tweets were scrapped from Twitter and analyzed using a number of proxy measures and Latent Dirichlet Allocation (LDA). The results revealed that interest in TD on Twitter has been generally increasing since the platform's early stages. Furthermore, TD‐related tweets were found to revolve around 11 distinct categories. The TD in games category was discovered to be the most popular category, followed by TD communication and TD repayment. The results highlight that TD is a diverse and overarching topic that contains many potential avenues for further exploration. Software engineering researchers, practitioners, and educators can utilize this study to help steer their TD‐related future efforts.

As part of the process of resolving issues submitted by users via bug reports, Android developers attempt to reproduce and observe the crashes described by the bug reports. Due to the low-quality of bug reports and the complexity of modern apps, the reproduction process is non-trivial and time-consuming. Therefore, automatic approaches that can help reproduce Android bug reports are in great need. However, current approaches to help developers automatically reproduce bug reports are only able to handle limited forms of natural language text and struggle to successfully reproduce crashes for which the initial bug report had missing or imprecise steps. In this paper, we introduce a new fully automated approach to reproduce crashes from Android bug reports that addresses these limitations. Our approach accomplishes this by leveraging natural language processing techniques to more holistically and accurately analyze the natural language in Android bug reports and designing new techniques, based on reinforcement learning, to guide the search for successful reproducing steps. We conducted an empirical evaluation of our approach on 77 real world bug reports. Our approach achieved 67% precision and 77% recall in accurately extracting reproduction steps from bug reports, reproduced 74% of the total bug reports, and reproduced 64% of the bug reports that contained missing steps, significantly outperforming state of the art techniques.

Software engineering ethics has been an interest of the software engineering community for over two decades, as evidenced by the creation of the joint ACM/IEEE-CS Soft-ware Engineering Code of Ethics. When reviewing the cur-rent software engineering literature, it was found that Stack Exchange question-and-answer (Q&A) websites are particularly good means of gaining a better understanding on a given subject: what topics are addressed within a subject area, the popularity of these topics, and the difficulty of these topics. As such, this paper utilizes Stack Exchange Q&A websites to review software engineering ethics and presents a case study that aims to provide a better understanding of the presence of software engineering ethics on three popular Stack Exchange Q&A websites, which are Stack Overflow (SO), Software Engineering (SE), and Project Management (PM). The study analyzes over 2,170 posts, using Latent Dirichlet Allocation (LDA), to better understand what ethic-related topics are discussed. Subsequently, the authors analyzed the popularity and difficulty of each identified topic. The study found that users discuss six ethic-related topics, which are as follows: web scraping, software quality, software security, open-source software usage, team and employers, and software billing. Moreover, the results revealed that software security is the most popular and the most difficult topic. This study highlights ethic-related challenges that are faced by Q&A website users. Software practitioners, researchers, and educators can utilize the results presented in this study to pursue new avenues when addressing pressing issues related to software engineering ethics.

Repaying all technical debt (TD) present in a system may be unfeasible, as there is typically a shortage in the resources allocated for TD repayment. Therefore, TD prioritization is essential to best allocate such resources to determine which TD items are to be repaid first and which items are to be delayed until later releases. This study conducts a systematic literature review (SLR) to identify and analyze the currently researched TD prioritization approaches. The employed search strategy strove to achieve high completeness through the identification of a quasi-gold standard set, which was used to establish a search string to automatically retrieve papers from select research databases. The application of selection criteria, along with forward and backward snowballing, identified 24 TD prioritization approaches. The analysis of the identified approaches revealed a scarcity of approaches that account for cost, value, and resources constraint and a lack of industry evaluation. Furthermore, this SLR unveils potential gaps in the current TD prioritization research, which future research may explore.

BACKGROUND: The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. METHOD: 'N-of-1-pathways' is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. RESULTS: Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03). CONCLUSIONS: The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies. SOFTWARE: http://lussierlab.org/publications/N-of-1-pathways.