Reviewerly/exHarmony huggingface dataset:
Understanding the relationships between the perceptions of burnout and instability in Software Engineering

Changes are common during the development process of software. These changes can increase the perception of instability of software developers. Understand the relationship between human factors and Software Engineering’s process is important to try to mitigate and prevent problems. One of these factors is burnout. Burnout is considered a disease that can impact several factors in Software Engineering, such as productivity, turnover, but mainly the health of the developers. Therefore, understanding the relationship between instability and burnout can help those involved to prevent and strategize so that developers can feel better and, consequently, produce more. The main objective of this work is to identify and describe the relationship between instability and burnout in the perception of members who participate in the software development process. To reach this objective, we conducted a cross-section survey with 411 respondents, which had its sampling by convenience and self-selection. In addition to identifying relationships between variables, confirmatory factor analysis techniques were also applied. The main results of this work are: The relationship between the perception of burnout and team, technological and task instability, which is positive and significant for the dimensions of exhaustion and cynicism. For the dimension of efficacy, it was negative and weak with technological and team instabilities and does not exist with task instability. In addition, exhaustion is perceived more frequently by respondents. The instability with the highest perception of frequency by the practitioners is that of the task. The results found are important because both the market and the academy can guide their efforts to reduce the perception of burnout and instability in software engineers. Furthermore, new research can be carried out to verify the impact of instability on developers, bringing a new perspective to monitor the perception of instability in the context of software development.
Towards Merge Conflict Resolution by Combining Existing Lines of Code

Software developers often need to combine their contributions. This operation is called merge. When the contributions happen at the same physical region in the source code, the merge is marked as conflicting and must be manually resolved by the developers. Existing studies explore why conflicts happen, their characteristics, and how they are resolved. In this paper, we investigate a specific subset of merge conflicts, which may be resolved using a combination of existing lines. We analyze 10,177 conflict chunks of popular projects that were resolved by combining existing lines, aiming at characterizing and finding patterns that developers frequently use to resolve them. We found that these conflicting chunks and their resolutions are usually small (they have a median of 6 LOC and 3 LOC, respectively). Moreover, 98.6% of the analyzed resolutions preserve the order of the lines in the conflicting chunks. We also found that 77.4% of the chunk resolutions do not interleave lines from different contributions more than once. These findings altogether, when used as heuristics for automatic merge resolution, could enable the reduction of the search space by 94.7%, paving the road for future search-based software engineering tools for this problem.
Flying over Brazilian Organizations with Zeppelin

Context: Software organizations have faced several challenges, such as the need for faster deliveries, frequent changes in requirements, lower tolerance to failures and the need to adapt to contemporary business models. Agile practices have allowed organizations to shorten development cycles and increase customer collaboration. However, this has not been enough. Organizations should evolve to continuous and data-driven development in a continuous software engineering approach. Continuous Software Engineering (CSE) consists of a set of practices and tools that support a holistic view of software development with the purpose of making it faster, iterative, integrated, continuous and aligned with business. Implementing CSE requires changes in the organization’s culture, practices and structure, which may not be easy. Objective: We aim to provide a preliminary picture of CSE adoption in Brazilian organizations. Method: We adapted and used Zeppelin, a diagnostic instrument of CSE adoption based on the Stairway to Heaven Model (StH) to perform a survey with 28 Brazilian organizations aiming at investigating the adoption of CSE practices. Results: The results indicate that organizations have better addressed agile and continuous deployment practices than the ones related to continuous integration and continuous experimentation, but this scenario changes a bit depending on the organization type. They also show that CSE adoption has been heterogeneous, but there are patterns in the adoption of some practices. Conclusion: Although the StH model proposes a sequential and evolutionary path for CSE adoption, organizations have not always followed it systematically. There are indeed CSE practices that depend on others and thus contribute to sequential implementation. However, organizations tend to adopt the practices gradually, covering different stages, and evolving according to the organization needs.
Use Cases for Software Development Analytics: A Case Study

Context Software engineering activities provide practitioners with large volumes of data that software analytics tools can use for many purposes, including defect prediction and effort estimation. However, the adoption of such tools depends on the information they provide and the real needs of practitioners. While existing research has focused on what developers need, the needs of managers are not well understood. Aims This study provides an in-depth analysis of the information needs of software practitioners from one organization that performs research, development, and innovation projects with industry partners. Understanding these practitioners’ needs enables the development of better analytics solutions to support managerial decision-making. Method We interviewed practitioners in leadership positions and analyzed the collected data using Grounded Theory coding techniques, i.e., open and selective coding. Results We identified 19 software analytics use cases and classified them into four dimensions: quality, people, project management, and knowledge management. We also elicited several indicators to meet the identified use cases and captured key aspects concerning the organization’s analytics scenario. Conclusions Although our results are particularly relevant to organizations similar to the one in which we conducted the study, they aim to serve as input for implementing new analytics solutions by practitioners and researchers in general.
UX Requirements Matters: Guidelines to Support Software Teams on the Writing of Acceptance Criteria

User Stories (USs) are artifacts that define requirements in agile software development and that are usually complemented by Acceptance Criteria (AC) which provide details about what should be implemented. These details often attempt to integrate User Experience (UX) requirements to their descriptions. This paper aims to introduce a set of guidelines that supports software teams to write UX requirements during the elaboration of AC. The guidelines are separated into Interaction Design (ID) and Visual Elements (VE). We conducted a case study with 10 agile teams that develop mobile applications that used the guidelines during a sprint. After that, we carried out a qualitative analysis of the UX requirements described in the USs/AC to evaluate the usage of the guidelines. We analyzed the guidelines’ acceptance regarding usefulness and ease of use based on feedback from the teams. We analyzed 242 AC and identified that 99 AC had UX requirements related to ID and 87 to VE. We identified that only 7 developers, spread over four teams, reported negative feedback about the guidelines. We conclude that teams were helped by the guidelines that respectively address details about the user’s interaction with the product, as well as the definition of the most adequate visual elements to enable this interaction.
TEMPY: Test Smell Detector for Python

Automated software testing is a process in software development that aims to extend software quality through test code. Thus, we can avoid manual and repetitive rework and have fast test runs. When writing test code, testers may execute bad practices, known as test smells, which are coding patterns that can negatively impact test quality in terms of maintainability, understandability, and defect detection effectiveness. Python became the most widely used programming language in the world in 2020, however, most research on test code quality is conducted for the Java language. In a previous work, we analyze occurrences of test smells in Python, so we developed TEMPY, an open-source test smells detection tool for Python, which detects 10 types of test smells, 5 of them not yet present in other Python detection tools. TEMPY achieved 100% accuracy on validation with our oracle. We evaluated TEMPY through interviews with 10 Python developers, obtaining 99% agreement on the detections pointed out by TEMPY. We hope TEMPY can support Python developers to improve test code quality, thereby supporting software testing activities.
An integrated complex adaptive governmental policy response to cyberthreats

This article analyzes the policy options available to governments for addressing the very costly economic impacts of cybersecurity threats. It contributes to complexity thinking in public policy. Complexity involves self-organization, emergence, feedback loops, and adaptation. We show that these are present with cyberthreats. In contrast to plans that involve a series of linear steps to a specific outcome, complexity thinking recommends adaptive design, which creates processes that involve coordinated decentralized capacity for experimentation and resilience. These are illustrated with an examination of the 2007 nation-wide cyberattack on Estonia and the lessons learned from this attack.
PREVALENCE OF MALARIA AND VECTOR ABUNDANCE IN AMICHI COMMUNITY, NNEWI SOUTH LOCAL GOVERNMENT AREA, ANAMBRA STATE, NIGERIA

Aim: Members of the Amichi community are easily prone to the bites of mosquitoes. This study was conducted in the Amichi village in Nigeria's Nnewi South L.G.A. to determine the prevalence of malaria and the abundance of mosquito vectors. Malaria parasites in the blood were found using blood preparation and microscopy.&#x0D; Methodology: 232 people from the town's ten settlements, including 135 men (61.15%) and 97 women (60.82%), took part in the survey. Participants were separated into 8 groups with ten-year intervals and ranged in age from 0 to 80. 142 (61.21%) of the 232 persons that were checked tested positive for malaria parasites. Plasmodium falciparum 135 (95.07%), Plasmodium malariae 6 (4.23%), and Plasmodium ovale 1 (0.70%) were the malaria parasites found in the study. Malaria prevalence was highest in the age group 51–60 years (16, 72.72%) and lowest in the age group 21–30 years (12, 50.00%). Age-related differences in the prevalence of malaria were not significant (p&gt;0.05; 11.07, df=14). Males had a little higher infection rate than females (59(60.82%)), although this difference was not statistically significant (0.548 d.f = 2, p&gt;0.05).&#x0D; Results: Farmers were marginally more likely to contract malaria than other occupational categories, but there were significant differences among them (P&gt;0.05). Anopheles gambiae larvae made up 51 (14.63%) of the 351 mosquito larvae collected, with Aedes albopictus larvae making up 117 (33.33%), and the result was statistically significant (694.6 d.f.=12; P0.05). Anopheles gambiae produced the highest yield of mosquitoes, 12 (46.15%), while Aedes albopictus produced the lowest, and the Pyrethrum Knockdown collection (PKC) of indoor biting and resting adult mosquitoes produced 26 mosquitoes in 4 villages.&#x0D; Conclusion: The results of the study showed that Anopheles gambiae breed in all villages thus every person in the town is at risk of malaria attack. Attributes of the rate of exposure of the study population to vector bites due to the nature of their work and standard of living from a factor in the transmission pattern of the disease. Mass education of the people n malaria infection, prevention, and control through environmental management will go a long way in helping to alleviate malaria infection in this and other communities
Perceptual Visibility Model for Temporal Contrast Changes in Periphery

Modeling perception is critical for many applications and developments in computer graphics to optimize and evaluate content generation techniques. Most of the work to date has focused on central (foveal) vision. However, this is insufficient for novel wide-field-of-view display devices, such as virtual and augmented reality headsets. Furthermore, the perceptual models proposed for the fovea do not readily extend to the off-center, peripheral visual field, where human perception is drastically different. In this paper, we focus on modeling the temporal aspect of visual perception in the periphery. We present new psychophysical experiments that measure the sensitivity of human observers to different spatio-temporal stimuli across a wide field of view. We use the collected data to build a perceptual model for the visibility of temporal changes at different eccentricities in complex video content. Finally, we discuss, demonstrate, and evaluate several problems that can be addressed using our technique. First, we show how our model enables injecting new content into the periphery without distracting the viewer, and we discuss the link between the model and human attention. Second, we demonstrate how foveated rendering methods can be evaluated and optimized to limit the visibility of temporal aliasing.
Texture-less surface reconstruction using shape-based image augmentation

The development of intelligent Robot-Assisted Minimally Invasive Surgery demands geometric reconstruction from endoscopic images. However, images of human tissue surfaces are commonly texture-less. Obtaining the dense depth map of a texture-less scene is very difficult because traditional feature-based 3D reconstruction methods cannot detect enough features to build dense correspondences for depth computation. Given this problem, this study proposes a novel reconstruction method based on our shape-based image augmentation method. The main contribution of this method is the provision of a novel means to resolve the texture-less problem mainly on the input data level. In our method, we first calculate two shape gradient maps using Shape-From-Shading (SFS) method and we build Fast Point Feature Histogram (FPFH) 3D descriptor map according to the shape. Second, a series of augmented images can be computed by combining shape gradient maps, FPFH map, and the original image with different weights. Finally, we detect features on the new augmented images. Based on feature calculated sparse depth information and SFS calculated dense shape information, we further integrate a rectified dense depth map. Experiments show that our method can reconstruct texture-less surfaces with good accuracy.
Mineral chemistry of chlorite in different geologic environments and its implications for porphyry Cu ± Au ± Mo deposits

• Mg, Fe, Al, and Ti are most different between chlorite PCDs and chlorite Barren . • Three diagrams are proposed to distinguish chlorite PCDs from chlorite Barren . • Redox states and formation temperatures result in the differences. In the porphyry Cu ± Au ± Mo deposits (PCDs), chlorite and its mineral assemblages are indicators for exploration. However, chlorite found in PCDs may have different origins because it is a common mineral in different geological environments. Therefore, it is necessary to identify the differences in the composition of PCDs-related chlorites (chlorite PCDs ) and chlorite formed in other geological environments (chlorite Barren ). Here, based on occurrences, mineral assemblages, and compositions of the chlorites from thirty PCDs and barren areas (including geothermal systems, chlorite GSs ; low-grade metamorphic rocks, chlorite LGMRs ; sedimentary rocks, chlorite SRs ), we used principal component analysis (PCA) and random forest (RF) methods to find discrimination approaches. Results show that chlorite PCDs and chlorite Barren differ in the Fe, Mg, Al, Ti, and possibly Mn, K, and Ca elements. Based on these differences, wt.% ratios of (MgO + 100*TiO 2 )/(FeO + 100*CaO), TiO 2 /(CaO + K 2 O + Na 2 O), Al 2 O 3 /(100*K 2 O*CaO), MgO/(100*CaO*Na 2 O), SiO 2 /100(CaO + K 2 O), and FeO/100TiO 2 and diagrams of Fe/(Fe + Mg) (a.p.f.u. ratio) vs TiO 2 /(Na 2 O + K 2 O + CaO) (wt.% ratio), Fe/(Fe + Mg) (a.p.f.u. ratio) vs MgO/(100*Na 2 O*CaO) (wt.% ratio), and Fe/(Fe + Mg) (a.p.f.u. ratio) vs SiO 2 /Al 2 O 3 (wt.% ratio) are proposed to distinguish chlorite PCDs from chlorite Barren . These discrimination approaches have better results in distinguishing chlorite PCDs from chlorite LGMRs and chlorite SRs but are less effective for chlorite GSs . By comparing the physicochemical conditions, the highly oxidized conditions and high temperature of the ore-related intrusions and fluid properties result in the compositional differences between chlorite PCDs and chlorite Barren . Although further researches remain to be done, this study provides potential approaches for identifying chlorite PCDs based on major elements.
Mask-guided explicit feature modulation for multispectral pedestrian detection

Multi-task learning of object detection and box-level segmentation is commonly formulated as an implicit feature modulation method, which suffers lacking task interaction. In this paper, a novel explicit feature modulation solution with two different mask infusion methods is proposed. The first modulation method is semantic feature enhancement of backbone, which is achieved by a novel mask-guided mutual attention module (MMA). The proposed MMA module can explicitly guide the feature maps towards a more semantic informative direction for focalizing centrality of pedestrian, which can significantly improve the performance. The second modulation method is confidence score enhancement of detection head, which is benefited from our proposed mask-guided score fusion module (MSF). The proposed MSF module collects information from the classification, IOU, centerness feature map and the learned mask, which can discriminate false and true positives more effectively. It is qualitatively validated that the modulated feature maps in both backbone and detection head become more semantically meaningful and robust to scale and occlusion. Our method achieves a considerable gain over the state-of-the-arts on the KAIST, CVC14 and FLIR datasets. Besides, it runs at 22 FPS in default setting, making it favorable in many practical scenarios.
Learning Spatiotemporal Interactions for User-Generated Video Quality Assessment

Distortions from spatial and temporal domains have been identified as the dominant factors that govern the visual quality. Though both have been studied independently in deep learning-based user-generated content (UGC) video quality assessment (VQA) by frame-wise distortion estimation and temporal quality aggregation, much less work has been dedicated to the integration of them with deep representations. In this paper, we propose a SpatioTemporal Interactive VQA (STI-VQA) model based upon the philosophy that video distortion can be inferred from the integration of both spatial characteristics and temporal motion, along with the flow of time. In particular, for each timestamp, both the spatial distortion explored by the feature statistics and local motion captured by feature difference are extracted and fed to a transformer network for the motion aware interaction learning. Meanwhile, the information flow of spatial distortion from the shallow layer to the deep layer is constructed adaptively during the temporal aggregation. The transformer network enjoys an advanced advantage for long-range dependencies modeling, leading to superior performance on UGC videos. Experimental results on five UGC video benchmarks demonstrate the effectiveness and efficiency of our STI-VQA model, and the source code will be available online at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/h4nwei/STI-VQA</uri> .
S2G2: Semi-Supervised Semantic Bird-Eye-View Grid-Map Generation Using a Monocular Camera for Autonomous Driving

Semantic bird-eye-view (BEV) grid map is a straightforward data representation for semantic environment perception. It can be conveniently integrated with downstream tasks, such as motion planning, trajectory prediction, etc. Most existing methods of semantic BEV grid-map generation adopt supervised learning, which requires extensive hand-labeled ground truth to achieve acceptable results. However, there exist limited datasets with hand-labeled ground truth for semantic BEV grid map generation, which hinders the research progress in this field. Moreover, manually labeling images is tedious and labor-intensive, and it is difficult to manually produce a semantic BEV map given a front-view image. To provide a solution to this problem, we propose a novel semi-supervised network to generate semantic BEV grid maps. Our network is end-to-end, which takes as input an image from a vehicle-mounted front-view monocular camera, and directly outputs the semantic BEV grid map. We evaluate our network on a public dataset. The experimental results demonstrate the superiority of our network over the state-of-the-arts.