Oleksandr Kosenkov, Ehsan Zabardast, Jannik Fischbach
et al.
Implementing privacy by design (PbD) according to the General Data Protection Regulation (GDPR) is met with a growing number of requirements engineering (RE) approaches. However, the question of which RE method for PbD fits best the goals of organisations remains a challenge. We report our endeavor to close this gap by synthesizing a goal-centric approach for PbD methods assessment. We used literature review, interviews, and validation with practitioners to achieve the goal of our study. As practitioners do not approach PbD systematically, we suggest that RE methods for PbD should be assessed against organisational goals, rather than process characteristics only. We hope that, when further developed, the goal-centric approach could support the development, selection, and tailoring of RE practices for PbD.
Ana B. M. Bett, Thais S. Nepomuceno, Edson OliveiraJr
et al.
Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.
Abstract The issue of safety in the use of machinery is an important issue for both manufacturers and employers. The current legislation imposes a number of obligations on manufacturers (essential requirements) and employers (minimum requirements) related to ensuring safety at every stage from design to operation. The study analyzes the possibility of applying selected Lean Manufacturing tools to improve occupational safety in the use of machinery (work equipment). The purpose of the study is to present the possibility of application of selected LM tools on operator safety, as well as to meet the requirements of legislators relating to safety related to the use of machinery. Implemented LM solutions improve communication and organization of work, and most importantly, reduce the risks associated with the operation of machinery and equipment.
Synthetic plastics harm the environment, so finding better materials is important. Researchers have studied PLA and starch to replace non-degradable petrochemicals. This research uses jackfruit seed starch and PLA to make degradable plastics. The tensile strength of degradable plastics was 3.35–9.08 MPa. Tests showed that the plastics were hydrophilic, meaning they bind to water and break down easily. The combination of the jackfruit seed starch with PLA-reinforced plastic had better thermal stability. Starch made the material swell more, while PLA made it swell less. Jackfruit seed starch-based plastics reinforced with PLA degraded in 52–56 days, meeting the ASTM 6400 standard.
Rubén González-González, Andrés García-Pérez, Gustavo Alonso Rodrigo
The improvement of the design of space launchers, with a consequent reduction in development costs, has not been achieved to the same extent as in the case of satellite designs, even when applying similar Concurrent Engineering processes and MBSE methodologies. The aim of this paper is to introduce the current research at “Universidad Politécnica de Madrid” onto increasing the design efficiency of small space launchers, which is in the preliminary conceptual phases. A new approach is developed based on physical models’ integration in a simulator using a MBSE framework to find an optimal balance between costs and design weights.
Niama Arreda, Hamza Isksioui, Haitam Boutahri
et al.
Interest in research on FDM systems using inexpensive materials like PLA and ABS is constantly increasing. In this regard, the scope of this study is narrowed to exclusively focus on PLA. To improve the surface finish of PLA printed products, it is important to have optimal values of the most important process parameters, notably layer height, temperature, and printing speed. The surface roughness is a critical aspect of additive manufacturing that directly impacts the functionality, aesthetics, and overall performance of printed parts. To accomplish the improvement of surface quality, the statistical method ANOVA (Analysis of Variance) is used to analyze data and identify the most relevant process parameters that impact roughness and dimensional precision. The response variables are identified during this study in order to define the optimal printing parameters for improving part quality and ensuring the best surface finishes. Additionally, the dimensional accuracy of the parts is analyzed in order to check the reliability and effectiveness of the optimum parameters. The results are validated through this additional assessment, which also provides insight into the capabilities and limitations of inexpensive FDM machines when the optimized parameters are used. In conclusion, this study emphasizes the significance of enhancing parameters to improve the performance of 3D printed components, providing insightful information about the potential of PLA as an inexpensive material for applications that need both high surface quality and precise dimensional control. According to the analysis, the thickness of the layers and printing speed have a significant role in the roughness for a better desired surface quality.
Sebastian Baltes, Florian Angermeir, Chetan Arora
et al.
Large Language Models (LLMs) are now ubiquitous in software engineering (SE) research and practice, yet their non-determinism, opaque training data, and rapidly evolving models threaten the reproducibility and replicability of empirical studies. We address this challenge through a collaborative effort of 22 researchers, presenting a taxonomy of seven study types that organizes the landscape of LLM involvement in SE research, together with eight guidelines for designing and reporting such studies. Each guideline distinguishes requirements (must) from recommended practices (should) and is contextualized by the study types it applies to. Our guidelines recommend that researchers: (1) declare LLM usage and role; (2) report model versions, configurations, and customizations; (3) document the tool architecture beyond the model; (4) disclose prompts, their development, and interaction logs; (5) validate LLM outputs with humans; (6) include an open LLM as a baseline; (7) use suitable baselines, benchmarks, and metrics; and (8) articulate limitations and mitigations. We complement the guidelines with an applicability matrix mapping guidelines to study types and a reporting checklist for authors and reviewers. We maintain the study types and guidelines online as a living resource for the community to use and shape (llm-guidelines$.$org).
Many organisational problems are addressed through systemic change and re-engineering of existing Information Systems rather than radical new design. In the face of widespread IT project failure, devising effective ways to tackle this type of change remains an open challenge. This work discusses the motivation, theoretical foundation, characteristics and evaluation of a novel framework - referred to as POE-$Δ$, which is rooted in design and engineering and is aimed at providing systematic support for representing, structuring and exploring change problems of a socio-technical nature, including implementing their solutions when they exist. We generalise an existing framework of greenfield design as problem solving for application to change problems. From a theoretical perspective,POE-$Δ$ is a strict extension to its parent framework, allowing the seamless integration of greenfield and brownfield design to tackle change problems. A Design Science Research methodology was applied over a decade to define and evaluate POE-$Δ$, with significant case study research conducted to evaluate the framework in its application to real-world change problems of varying criticality and complexity. The results show that POE-$Δ$ exhibits desirable characteristics of a design approach to organisational change and can bring tangible benefits when applied in practice as a holistic and systematic approach to change in socio-technical contexts.
Martin Obaidi, Lukas Nagel, Alexander Specht
et al.
Software development is a collaborative task. Previous research has shown social aspects within development teams to be highly relevant for the success of software projects. A team's mood has been proven to be particularly important. It is paramount for project managers to be aware of negative moods within their teams, as such awareness enables them to intervene. Sentiment analysis tools offer a way to determine the mood of a team based on textual communication. We aim to help developers or stakeholders in their choice of sentiment analysis tools for their specific purpose. Therefore, we conducted a systematic mapping study (SMS). We present the results of our SMS of sentiment analysis tools developed for or applied in the context of software engineering (SE). Our results summarize insights from 106 papers with respect to (1) the application domain, (2) the purpose, (3) the used data sets, (4) the approaches for developing sentiment analysis tools, (5) the usage of already existing tools, and (6) the difficulties researchers face. We analyzed in more detail which tools and approaches perform how in terms of their performance. According to our results, sentiment analysis is frequently applied to open-source software projects, and most approaches are neural networks or support-vector machines. The best performing approach in our analysis is neural networks and the best tool is BERT. Despite the frequent use of sentiment analysis in SE, there are open issues, e.g. regarding the identification of irony or sarcasm, pointing to future research directions. We conducted an SMS to gain an overview of the current state of sentiment analysis in order to help developers or stakeholders in this matter. Our results include interesting findings e.g. on the used tools and their difficulties. We present several suggestions on how to solve these identified problems.
Paloma Guenes, Rafael Tomaz, Bianca Trinkenreich
et al.
Research shows that more than half of software professionals experience the Impostor Phenomenon (IP), with a notably higher prevalence among women compared to men. IP can lead to mental health consequences, such as depression and burnout, which can significantly impact personal well-being and software professionals' productivity. This study investigates how IP manifests among software professionals across intersections of gender with race/ethnicity, marital status, number of children, age, and professional experience. Additionally, it examines the well-being of software professionals experiencing IP, providing insights into the interplay between these factors. We analyzed data collected through a theory-driven survey (n = 624) that used validated psychometric instruments to measure IP and well-being in software engineering professionals. We explored the prevalence of IP in the intersections of interest. Additionally, we applied bootstrapping to characterize well-being within our field and statistically tested whether professionals of different genders suffering from IP have lower well-being. The results show that IP occurs more frequently in women and that the prevalence is particularly high among black women as well as among single and childless women. Furthermore, regardless of gender, software engineering professionals suffering from IP have significantly lower well-being. Our findings indicate that effective IP mitigation strategies are needed to improve the well-being of software professionals. Mitigating IP would have particularly positive effects on the well-being of women, who are more frequently affected by IP.
Sirojiddin Sadullayev, Suvankul Ravshanov, Jamol Mirzayev
et al.
The impact of flour particle size and starch damage on the baking properties of wheat flour cultivated in dry climates, focusing on Uzbekistan, was investigated. Given the critical role of bread and flour products in Central Asian diets, understanding grain cultivation’s influence on these products is imperative. Dry climates affect wheat quality, particularly its protein and glutenin content, influencing dough resistance and bread appearance. This study evaluated how flour particle size and starch damage affect baking properties using wheat flour grown in semi-arid regions, aiming to assist wheat growers in post-harvest irrigation decisions. Through a combination of chemical and physico-chemical methods, including particle size analysis, damaged starch measurement, and baking tests, this study elucidated the relationship between flour characteristics and baking performance. Results indicate that smaller flour particle sizes enhance dough-mixing properties, but may adversely affect crumb firmness. Furthermore, high levels of starch damage negatively impact flour quality and baking properties. Importantly, this study underscores the significance of understanding these factors in optimizing wheat cultivation and flour processing for improved bread quality in dry climates. Specifically, results show that for high-grade flour (Sardor), the control sample had a gluten content of 25.6%, with a drop number of 190 and a degree of starch damage of 26.9 units. Conversely, flour samples from locally grown soft wheat demonstrated higher starch damage, ranging from 3.4 to 3.9 units compared to imported samples. Additionally, regression analysis revealed significant coefficients for particle size and starch damage on the amount of wet gluten washed from these flour samples.
Rik Vaerenberg, Douw Marx, Seyed Ali Hosseinli
et al.
Gear pitting is a common gearbox failure mode that can lead to unplanned machine downtime, inefficient power transmission and a higher risk of sudden catastrophic failure. Consequently, there is strong incentive to create machine learning models that are capable of detecting and quantifying the severity of gearbox pitting faults. The performance of machine learning models is however highly dependent on the availability of training data and since training data for a wide variety of different operating conditions and fault severities is rarely available in practice, machine learning models must be designed to be robust to unseen operating conditions and fault severities. Furthermore, models should be capable of identifying data outside of the training data distribution and adjusting the confidence in a prediction accordingly. This work presents a strategy for pitting severity estimation in gearboxes under unseen operating conditions and fault severities in response to the PHM North America 2023 Conference Data Challenge. The strategy includes the design of dedicated validation sets for quantifying model performance on unseen data, an investigation into the most appropriate preprocessing methods, and a specialized convolutional neural network with an integrated out of distribution detection model for identifying samples from foreign operating conditions and fault severities. The results show that the best models are capable of some generalization to unseen operating conditions, but the generalization to unseen pitting severities is more challenging.
Engineering machinery, tools, and implements, Systems engineering
Renato Montillo, Maria Cristina Morani, Oreste Fecarotta
et al.
Recent research has focused on the dynamic control and regulation of hydraulic devices like pumps and turbines to enhance the efficiency of water systems. These devices are adjusted to maintain nearly optimal hydraulic conditions and operating efficiency, although achieving both can be challenging due to factors like machine type and changes in distribution patterns. Incipient cavitation, which can cause mechanical damage and reduce efficiency, presents a specific challenge. It produces a distinct noise which this study aims to detect through a proposed methodology. Using the LES WALE model in OpenFOAM and Lighthill’s acoustic analogy, this research simulates and analyzes the noise generated by the dynamic of a confined flow. This work aims to be the starting point for more complex models.
Sinclair Hudson, Sophia Jit, Boyue Caroline Hu
et al.
Large Language Models (LLMs) are rapidly becoming ubiquitous both as stand-alone tools and as components of current and future software systems. To enable usage of LLMs in the high-stake or safety-critical systems of 2030, they need to undergo rigorous testing. Software Engineering (SE) research on testing Machine Learning (ML) components and ML-based systems has systematically explored many topics such as test input generation and robustness. We believe knowledge about tools, benchmarks, research and practitioner views related to LLM testing needs to be similarly organized. To this end, we present a taxonomy of LLM testing topics and conduct preliminary studies of state of the art and practice approaches to research, open-source tools and benchmarks for LLM testing, mapping results onto this taxonomy. Our goal is to identify gaps requiring more research and engineering effort and inspire a clearer communication between LLM practitioners and the SE research community.
Jialiang Wei, Anne-Lise Courbis, Thomas Lambolais
et al.
Graphical User Interfaces (GUIs) are central to app development projects. App developers may use the GUIs of other apps as a means of requirements refinement and rapid prototyping or as a source of inspiration for designing and improving their own apps. Recent research has thus suggested retrieving relevant GUI designs that match a certain text query from screenshot datasets acquired through crowdsourced or automated exploration of GUIs. However, such text-to-GUI retrieval approaches only leverage the textual information of the GUI elements, neglecting visual information such as icons or background images. In addition, retrieved screenshots are not steered by app developers and lack app features that require particular input data. To overcome these limitations, this paper proposes GUing, a GUI search engine based on a vision-language model called GUIClip, which we trained specifically for the problem of designing app GUIs. For this, we first collected from Google Play app introduction images which display the most representative screenshots and are often captioned (i.e.~labelled) by app vendors. Then, we developed an automated pipeline to classify, crop, and extract the captions from these images. This resulted in a large dataset which we share with this paper: including 303k app screenshots, out of which 135k have captions. We used this dataset to train a novel vision-language model, which is, to the best of our knowledge, the first of its kind for GUI retrieval. We evaluated our approach on various datasets from related work and in a manual experiment. The results demonstrate that our model outperforms previous approaches in text-to-GUI retrieval achieving a Recall@10 of up to 0.69 and a HIT@10 of 0.91. We also explored the performance of GUIClip for other GUI tasks including GUI classification and sketch-to-GUI retrieval with encouraging results.
Software engineering (SE) is full of abstract concepts that are crucial for both researchers and practitioners, such as programming experience, team productivity, code comprehension, and system security. Secondary studies aimed at summarizing research on the influences and consequences of such concepts would therefore be of great value. However, the inability to measure abstract concepts directly poses a challenge for secondary studies: primary studies in SE can operationalize such concepts in many ways. Standardized measurement instruments are rarely available, and even if they are, many researchers do not use them or do not even provide a definition for the studied concept. SE researchers conducting secondary studies therefore have to decide a) which primary studies intended to measure the same construct, and b) how to compare and aggregate vastly different measurements for the same construct. In this experience report, we discuss the challenge of study selection in SE secondary research on latent variables. We report on two instances where we found it particularly challenging to decide which primary studies should be included for comparison and synthesis, so as not to end up comparing apples with oranges. Our report aims to spark a conversation about developing strategies to address this issue systematically and pave the way for more efficient and rigorous secondary studies in software engineering.
Acid Red 88 (AR88) is an azo dye highly used in the textile industry. This industry generates high volumes of wastewater with recalcitrant properties that can persist in nature for many years. This work intends to use a statistical model to better predict and understand the influence of different operational conditions. A Box-Behnken response surface methodology (RSM) was used, in which variables (H<sub>2</sub>O<sub>2</sub>, Fe<sup>2+</sup>, and radiation intensity) were changed. At the same time, the RSM model allowed the assessment of several advanced oxidation processes (AOPs). The results exhibited the photo-Fenton process as the most efficient, and the best operational conditions ([AR88] = 0.125 mM, pH = 3.0, [H<sub>2</sub>O<sub>2</sub>] = 7.9 mM, [Fe<sup>2+</sup>] = 0.22 mM, time = 30 min) were used in four different reactors (UV-C, UV-A, ultrasound, and solar). US reactors achieved high AR88 removal (98.2%, 50 min), similar to UV-C and UV-A (97.8 and 98.2%, respectively, 60 min). A solar reactor is concluded to be the most feasible choice, with 98.4% AR88 removal after 25 min.
The municipal solid waste management is key for preserving the environment, protecting public health, and maintaining public cleanliness. Apart from these benefits, developing countries are more attracted to such systems as they offer an economical solution for waste management. This article intends to present a cost-effective and optimized waste management solution for Islamabad, Pakistan. The key steps of this research include; 1. acquisition of data; 2. transformation of data into coordinate form; 3. modeling of optimized waste collection routes and transportation; 4. modification of the capacitated vehicle routing algorithm to yield the optimized vehicle routes. The simulations are performed in MATLAB 2017B. Trial results show the efficacy of the recommended method.
Sudhir Jain Prathik, Athimoolam Sundaramahalingam, Maddur Eswara Nithyashree
et al.
The present research focuses on the machining of grade 2 titanium material using the Wire Electric Discharge Machining (WEDM) process by means of L<sub>16</sub> Orthogonal Array (OA). This study investigates numerous process parameters, including pulse on time, current, pulse off time, voltage, bed speed and flush rate. The voltage and flush rate were kept constant throughout the experiment, while the other four parameters were varied for the machining process. In this study, a 0.18 mm molybdenum wire was utilized as the electrode material. Initially, this research aimed to optimize the process parameters to discern their impact on machining characteristics (Surface Roughness and Electrode Wear) as well as on machining performance (Acoustic Emission Signals). Subsequently, simpler functional relationship plots were generated between these parameters to recognize the potential information about the machining characteristics and machining performance. The straightforward approach lacks the capability to furnish information regarding the condition of the material (Surface Roughness), the tool (Electrode Wear) and the signals (Acoustic Emission). Hence, to estimate the experimental values the numerical tools viz., Group Method of Data Handling (GMDH) and Artificial Neural Network (ANN) were used. Upon comparing the predictive performance of ANN and GMDH, it became evident that the ANN’s predictions using 70% of the data for training displayed a higher correlation with the experimental values compared to the GMDH.