Work

Job postings that explicitly encourage “people with migration backgrounds” or those who “experience discrimination” to apply, were a good start once. But they do not solve the problem. Sure, an injustice is being identified here that is imperative to counteract. Unless rules like quotas are established, however, such postings ultimately peter out as a merely symbolic gesture. In the end, it is not the well-intended statement that counts, but who is being hired. And who isn’t

Foreward to the Translations

Eure Heimat ist unser Albtraum [Your Homeland is our Nightmare] is the title of our collective work: essays by fourteen German-language authors, framed at the beginning of 2019 as a sort of answer to these developments. Because as one can imagine, this concept of homeland is a nightmare for marginalized groups in our society. But not just for them. That’s the reason why two words on the original cover of the book are colored the same shade of purple as the book cover itself. Because it is not the editors and authors of this book who decide where “we” ends and “you” begins. Every reader decides this for themselves: Do I want to live in a society oriented around völkisch, racist, antisemitic, sexist, heteronormative, and trans-antagonistic structures? Or would I rather be a part of a society in which every individual—whether black and/or Jewish and/or Muslim and/or woman and/or queer and/or non-binary and/or poor and/or differently abled—is treated equally

Exploring Emerging Technologies for Requirements Elicitation Interview Training: Empirical Assessment of Robotic and Virtual Tutors

Requirements elicitation interviews are a widely adopted technique, where the interview success heavily depends on the interviewer's preparedness and communication skills. Students can enhance these skills through practice interviews. However, organizing practice interviews for many students presents scalability challenges, given the time and effort required to involve stakeholders in each session. To address this, we propose REIT, an extensible architecture for Requirements Elicitation Interview Training system based on emerging educational technologies. REIT has components to support both the interview phase, wherein students act as interviewers while the system assumes the role of an interviewee, and the feedback phase, during which the system assesses students' performance and offers contextual and behavioral feedback to enhance their interviewing skills. We demonstrate the applicability of REIT through two implementations: RoREIT with a physical robotic agent and VoREIT with a virtual voice-only agent. We empirically evaluated both instances with a group of graduate students. The participants appreciated both systems. They demonstrated higher learning gain when trained with RoREIT, but they found VoREIT more engaging and easier to use. These findings indicate that each system has distinct benefits and drawbacks, suggesting that REIT can be realized for various educational settings based on preferences and available resources.Comment: Author submitted manuscrip

RoboREIT: an Interactive Robotic Tutor with Instructive Feedback Component for Requirements Elicitation Interview Training

[Context] Interviewing stakeholders is the most popular requirements elicitation technique among multiple methods. The success of an interview depends on the collaboration of the interviewee which can be fostered through the interviewer's preparedness and communication skills. Mastering these skills requires experience and practicing interviews. [Problem] Practical training is resource-heavy as it calls for the time and effort of a stakeholder for each student which may not be feasible for a large number of students. [Method] To address this scalability problem, this paper proposes RoboREIT, an interactive Robotic tutor for Requirements Elicitation Interview Training. The humanoid robotic component of RoboREIT responds to the questions of the interviewer, which the interviewer chooses from a set of predefined alternatives for a particular scenario. After the interview session, RoboREIT provides contextual feedback to the interviewer on their performance and allows the student to inspect their mistakes. RoboREIT is extensible with various scenarios. [Results] We performed an exploratory user study to evaluate RoboREIT and demonstrate its applicability in requirements elicitation interview training. The quantitative and qualitative analyses of the users' responses reveal the appreciation of RoboREIT and provide further suggestions about how to improve it. [Contribution] Our study is the first in the literature that utilizes a social robot in requirements elicitation interview education. RoboREIT's innovative design incorporates replaying faulty interview stages and allows the student to learn from mistakes by a second time practicing. All participants praised the feedback component, which is not present in the state-of-the-art, for being helpful in identifying the mistakes. A favorable response rate of 81% for the system's usefulness indicates the positive perception of the participants.Comment: Author submitted manuscrip

Evaluating Classifiers in SE Research: The ECSER Pipeline and Two Replication Studies

Context: Automated classifiers, often based on machine learning (ML), are increasingly used in software engineering (SE) for labelling previously unseen SE data. Researchers have proposed automated classifiers that predict if a code chunk is a clone, if a requirement is functional or non-functional, if the outcome of a test case is non-deterministic, etc. Objective: The lack of guidelines for applying and reporting classification techniques for SE research leads to studies in which important research steps may be skipped, key findings might not be identified and shared, and the readers may find reported results (e.g., precision or recall above 90%) that are not a credible representation of the performance in operational contexts. The goal of this paper is to advance ML4SE research by proposing rigorous ways of conducting and reporting research. Results: We introduce the ECSER (Evaluating Classifiers in Software Engineering Research) pipeline, which includes a series of steps for conducting and evaluating automated classification research in SE. Then, we conduct two replication studies where we apply ECSER to recent research in requirements engineering and in software testing. Conclusions: In addition to demonstrating the applicability of the pipeline, the replication studies demonstrate ECSER’s usefulness: not only do we confirm and strengthen some findings identified by the original authors, but we also discover additional ones. Some of these findings contradict the original ones

Effects of Immune Complexes on Holotranscobalamine Assay of Vitamin B-12 Deficiency in Myeloproliferative Disorders

In myeloproliferative disorders (MPDs), vitamin B-12 levels are measured falsely elevated with conventional methods due to increased carrier protein synthesis. HoloTranscobalamine (HoloTC) assay is a first-choice method for detecting true vitamin B-12 deficiency in MPDs. Our aim was to determine effects of immune complexes on HoloTC assay. This is a cross-section study. Vitamin B-12 levels in 61 patients with myeloproliferative disorders were measured by both electrochemical immunoassay and HoloTC assay. The HoIoTC cutoff was greater than 35 pmol/L. HoloTC assay for each sample were repeated after polyethylene glycol (PEG) treatment to exclude IgG, IgA and IgM type immune complexes. Also, methylmalonic acid, folate, homocystein, liver, and kidney function tests were obtained. Methylmalonic acid test showed that 42 patients (68.9%) had vitamin B-12 deficiency. Vitamin B-12 levels by HoIoTC assay decreased by 19.2 +/- 11.28% in essential thrombocytosis, 40.0 +/- 9.39% in chronic myeloid leukemia, 30.9 +/- 14.62% in myelofibrosis and 21.2 +/- 11.55% in polycythemia vera patients after PEG treatment. There was significant difference between the averages of groups (p< 0.01). Methylmalonic Acid Test was used as the B-12 status variable. The comparison of ROC curves of HoIoTC before and after PEG showed no statistically significance between area under curves. The optimum cut-off points for both HoloTC before and after PEG were 40.6 pmol/L and 32.1 pmol/L, respectively. Immune complexes may have some effect on HoIoTC assay which has been recently reported to have a superior diagnostic accuracy for vitamin Biz deficiency in patients with MPDs. Although exclusion of immune complexes did not improve its diagnostic performances, effects of exclusion were significantly different between subgroups of MPDs

Preeklampsinin Patogenezinde Maternal Oksidatif Stres, Demir/Çinko, Bakır/Çinko Oranları ve Eser Element Düzeylerinin Rolü

Purpose:Preeclampsia (PE) is a complex disease and the underlying mechanisms are not known, yet. It is well known that oxidative stress and trace elements play a role in the pathogenesis of various diseases. Several studies have shown that the levels and proportions of trace elements are closely related to the severity of the disease. The aim of the study was to investigate the changes in some characteristics parameters, serum zinc, iron, copper levels, and copper/zinc and iron/zinc ratios and plasma lipid peroxidation levels in patients with mild and severe preeclampsiaAma&ccedil;: Preeklampsi (PE) kompleks bir hastalıktır ve hastalığın patogenezinde yer alan mekanizmalar hen&uuml;z aydınlatılamamıştır. Oksidatif stres ve eser elementlerin &ccedil;eşitli hastalıkların patogenezinde rol oynadığı iyi bilinmektedir. Yapılan &ccedil;eşitli &ccedil;alışmalarda, eser elementlerin d&uuml;zeylerinin ve oranlarının, hastalığın şiddeti ile yakından ilişkili olduğunu g&ouml;stermiştir. Bu &ccedil;alışmanın amacı, hafif ve şiddetli preeklamptik hastalarda bazı karakteristik &ouml;zelliklerin, serum &ccedil;inko, demir, bakır d&uuml;zeyleri, bakır/&ccedil;inko ve demir/&ccedil;inko oranları ile plazma lipid peroksidasyon d&uuml;zeylerini değişikliklerini araştırmaktı

Requirements Classification with Interpretable Machine Learning and Dependency Parsing

Requirements classification is a traditional application of machine learning (ML) to RE that helps handle large requirements datasets. A prime example of an RE classification problem is the distinction between functional and non-functional (quality) requirements. State-of-the-art classifiers build their effectiveness on a large set of word features like text n-grams or POS n-grams, which do not fully capture the essence of a requirement. As a result, it is arduous for human analysts to interpret the classification results by exploring the classifier's inner workings. We propose the use of more general linguistic features, such as dependency types, for the construction of interpretable ML classifiers for RE. Through a feature engineering effort, in which we are assisted by modern introspection tools that reveal the hidden inner workings of ML classifiers, we derive a set of 17 linguistic features. While classifiers that use our proposed features fit the training set slightly worse than those that use high-dimensional feature sets, our approach performs generally better on validation datasets and it is more interpretable

Replication in requirements engineering: the NLP for RE case

Natural language processing (NLP) techniques have been widely applied in the requirements engineering (RE) field to support tasks such as classification and ambiguity detection. Despite its empirical vocation, RE research has given limited attention to replication of NLP for RE studies. Replication is hampered by several factors, including the context specificity of the studies, the heterogeneity of the tasks involving NLP, the tasks’ inherent hairiness, and, in turn, the heterogeneous reporting structure. To address these issues, we propose a new artifact, referred to as ID-Card, whose goal is to provide a structured summary of research papers emphasizing replication-relevant information. We construct the ID-Card through a structured, iterative process based on design science. In this article: (i) we report on hands-on experiences of replication; (ii) we review the state-of-the-art and extract replication-relevant information: (iii) we identify, through focus groups, challenges across two typical dimensions of replication: data annotation and tool reconstruction; and (iv) we present the concept and structure of the ID-Card to mitigate the identified challenges. This study aims to create awareness of replication in NLP for RE. We propose an ID-Card that is intended to foster study replication but can also be used in other contexts, e.g., for educational purposes.This article was partially supported by the following projects and grants: Italian MUR–PRIN 2020TL3X8X project T-LADIES (Typeful Language Adaptation for Dynamic, Interacting and Evolving Systems); EU Project CODECS GA 101060179, by the MOST – Sustainable Mobility National Research Center and received funding from the European Union Next- Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1033 17/06/2022, CN00000023); KKS foundation through the S.E.R.T. Research Profile project at Blekinge Institute of Technology; Spanish Ministerio de Ciencia e Innovación under project/funding scheme PID2020-117191RBI00/ AEI/10.13039/501100011033.Peer ReviewedPostprint (published version