Continuous Software Development Research Articles

Critical assessment of protein intrinsic disorder prediction (CAID) - Results of round 2.

Protein intrinsic disorder (ID) is a complex and context-dependent phenomenon that covers a continuum between fully disordered states and folded states with long dynamic regions. The lack of a ground truth that fits all ID flavors and the potential for order-to-disorder transitions depending on specific conditions makes ID prediction challenging. The CAID2 challenge aimed to evaluate the performance of different prediction methods across different benchmarks, leveraging the annotation provided by the DisProt database, which stores the coordinates of ID regions when there is experimental evidence in the literature. The CAID2 challenge demonstrated varying performance of different prediction methods across different benchmarks, highlighting the need for continued development of more versatile and efficient prediction software. Depending on the application, researchers may need to balance performance with execution time when selecting a predictor. Methods based on AlphaFold2 seem to be good ID predictors but they are better at detecting absence of order rather than ID regions as defined in DisProt. The CAID2 predictors can be freely used through the CAID Prediction Portal, and CAID has been integrated into OpenEBench, which will become the official platform for running future CAID challenges.

Driving the Technology Value Stream by Analyzing App Reviews

An emerging feature of mobile application software is the need to quickly produce new versions to solve problems that emerged in previous versions. This helps adapt to changing user needs and preferences. In a continuous software development process, the user reviews collected by the apps themselves can play a crucial role to detect which components need to be reworked. This paper proposes a novel framework that enables software companies to drive their technology value stream based on the feedback (or reviews) provided by the end-users of an application. The proposed end-to-end framework exploits different Natural Language Processing (NLP) tasks to best understand the needs and goals of the end users. We also provide a thorough and in-depth analysis of the framework, the performance of each of the modules, and the overall contribution in driving the technology value stream. An analysis of reviews with sixteen popular Android Play Store applications from various genres over a long period of time provides encouraging evidence of the effectiveness of the proposed approach.

CellTrackVis: interactive browser-based visualization for analyzing cell trajectories and lineages

BackgroundAutomatic cell tracking methods enable practitioners to analyze cell behaviors efficiently. Notwithstanding the continuous development of relevant software, user-friendly visualization tools have room for further improvements. Typical visualization mostly comes with main cell tracking tools as a simple plug-in, or relies on specific software/platforms. Although some tools are standalone, limited visual interactivity is provided, or otherwise cell tracking outputs are partially visualized.ResultsThis paper proposes a self-reliant visualization system, CellTrackVis, to support quick and easy analysis of cell behaviors. Interconnected views help users discover meaningful patterns of cell motions and divisions in common web browsers. Specifically, cell trajectory, lineage, and quantified information are respectively visualized in a coordinated interface. In particular, immediate interactions among modules enable the study of cell tracking outputs to be more effective, and also each component is highly customizable for various biological tasks.ConclusionsCellTrackVis is a standalone browser-based visualization tool. Source codes and data sets are freely available at http://github.com/scbeom/celltrackvis with the tutorial at http://scbeom.github.io/ctv_tutorial.

Approach to automation of fuzzing-testing processes in the cycle of continuous software development

Approach to automation of fuzzing-testing processes in the cycle of continuous software development

Aesthetic Attribute Assessment of Images Numerically on Mixed Multi-attribute Datasets

With the continuous development of social software and multimedia technology, images have become a kind of important carrier for spreading information and socializing. How to evaluate an image comprehensively has become the focus of recent researches. The traditional image aesthetic assessment methods often adopt single numerical overall assessment scores, which has certain subjectivity and can no longer meet the higher aesthetic requirements. In this article, we construct an new image attribute dataset called aesthetic mixed dataset with attributes (AMD-A) and design external attribute features for fusion. Besides, we propose an efficient method for image aesthetic attribute assessment on mixed multi-attribute dataset and construct a multitasking network architecture by using the EfficientNet-B0 as the backbone network. Our model can achieve aesthetic classification, overall scoring, and attribute scoring. In each sub-network, we improve the feature extraction through ECA channel attention module. As for the final overall scoring, we adopt the idea of the teacher-student network and use the classification sub-network to guide the aesthetic overall fine-grain regression. Experimental results, using the MindSpore, show that our proposed method can effectively improve the performance of the aesthetic overall and attribute assessment.

Approaches for Documentation in Continuous Software Development

It is common practice for practitioners in industry as well as for ICT/CS students to keep writing – and reading ¬– about software products to a bare minimum. However, refraining from documentation may result in severe issues concerning the vaporization of knowledge regarding decisions made during the phases of design, build, and maintenance. In this article, we distinguish between knowledge required upfront to start a project or iteration, knowledge required to complete a project or iteration, and knowledge required to operate and maintain software products. With `knowledge', we refer to actionable information. We propose three approaches to keep up with modern development methods to prevent the risk of knowledge vaporization in software projects. These approaches are `Just Enough Upfront' documentation, `Executable Knowledge', and `Automated Text Analytics' to help record, substantiate, manage and retrieve design decisions in the aforementioned phases. The main characteristic of `Just Enough Upfront' documentation is that knowledge required upfront includes shaping thoughts/ideas, a codified interface description between (sub)systems, and a plan. For building the software and making maximum use of progressive insights, updating the specifications is sufficient. Knowledge required by others to use, operate and maintain the product includes a detailed design and accountability of results. `Executable Knowledge' refers to any executable artifact except the source code. Primary artifacts include Test Driven Development methods and infrastructure-as-code, including continuous integration scripts. A third approach concerns `Automated Text Analysis' using Text Mining and Deep Learning to retrieve design decisions.

Handling Complexity in Modern Software Engineering: Editorial Introduction to Issue 32 of CSIMQ

The potential of the Internet and related digital technologies, such as the Internet of Things (IoT), cognition and artificial intelligence, data analytics, services computing, cloud computing, mobile systems, collaboration networks, and cyber-physical systems, are both strategic drivers and enablers of modern digital platforms with fast-evolving ecosystems of intelligent services for digital products. This issue of CSIMQ presents three recent articles on modern software engineering. First, we focus on continuous software development and place it in the context of software architectures and digital transformation. The first contribution is followed by the description of the basis of specific security requirements and adequate digital monitoring mechanisms. Finally, we present a practical example of the digital management of livestock farming.

Analysis of Improving Effect of Running APP on College Students' Physique by Using Student Data Mining Technology.

With the continuous development of sports software, college students, as the most advanced social new ideological group, running APP gradually enter the study and life of college students. Based on students' DM (data mining), this paper analyzes the influence of running apps on the improvement of college students' physique, chooses DT (decision tree) algorithm to construct the structure according to the characteristics of the data used, obtains students' DM model, and prunes it by using substitution error rate and PEP (pessimistic error pruning). The results show that the results of intra-group comparison among boys show that the scores of 1000 m in the intervention group have no obvious change compared with those before the intervention, and the difference is not statistically significant (P=0.516). The 800 m scores of girls in the intervention group were better than those in the control group, and the difference was statistically significant (P=0.03). The results of intra-group comparison showed that there was no significant difference in the scores of the intervention group before and after the intervention, and the difference was not statistically significant (P=0.32). After the experiment, the vital capacity scores of boys and girls in APP intervention group and control group were improved, with statistical significance (P < 0.05). The conclusion shows that running APP can improve students' speed level, cultivate students' endurance level, and improve students' physical health.

Benefits of continuous software development

Benefits of continuous software development

A mapping study on documentation in Continuous Software Development

With an increase in Agile, Lean, and DevOps software methodologies over the last years (collectively referred to as Continuous Software Development (CSD)), we have observed that documentation is often poor. This work aims at collecting studies on documentation challenges, documentation practices, and tools that can support documentation in CSD. A systematic mapping study was conducted to identify and analyze research on documentation in CSD, covering publications between 2001 and 2019. A total of 63 studies were selected. We found 40 studies related to documentation practices and challenges, and 23 studies related to tools used in CSD. The challenges include: informal documentation is hard to understand, documentation is considered as waste, productivity is measured by working software only, documentation is out-of-sync with the software and there is a short-term focus. The practices include: non-written and informal communication, the usage of development artifacts for documentation, and the use of architecture frameworks. We also made an inventory of numerous tools that can be used for documentation purposes in CSD. Overall, we recommend the usage of executable documentation, modern tools and technologies to retrieve information and transform it into documentation, and the practice of minimal documentation upfront combined with detailed design for knowledge transfer afterwards. It is of paramount importance to increase the quantity and quality of documentation in CSD. While this remains challenging, practitioners will benefit from applying the identified practices and tools in order to mitigate the stated challenges.

MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging

Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.

Continuous Software Engineering and Unit Testing: From Theory to Practice

With the Agile development approach, the software industry has moved to a more flexible and continuous Software Development Life Cycle (SDLC), which integrates the stages of development, delivery and deployment. This trend has exposed a tendency of increasing reliance on both unit testing and test automation for the fundamental quality-activities during the code development. To implement Continuous Software Engineering (CSE), it is vital to assure that unit-testing activities are an integral and well-defined part of a continuous process. This paper focuses on the initial role of actual testing – viewing unit testing as a quality indicator during the development life cycle. We review the definition of unit-testing from the CSE world, and describe a qualitative study in which we examined implementation of unit testing in three software companies that recently migrated to CSE methodology. The results from the qualitative study corroborate our argument that under the continues approach, quality-based development practices such as unit testing are of increasing importance, lacking common set of measurements and KPI's. A possible explanation to this may be the role of continuous practices as well as unit testing in the software engineering curriculum

Reducing distracted pedestrian behavior using Bluetooth beacon technology: A crossover trial

BackgroundThe number of fatal pedestrian injuries in the United States has steadily increased over the past decade. Multiple factors likely contribute to this trend, but the growth of pedestrians distracted by mobile devices is widely hypothesized to play a major role. Existing strategies to reduce distracted pedestrian behavior are few and mostly ineffective. The present study evaluated StreetBit, a mostly-passive primary prevention program to reduce distracted pedestrian behavior by alerting distracted pedestrians directly on their smartphone when they approach an intersection, reminding them to attend to traffic as they crossed. Methods385 individuals who regularly crossed a target street corner at an urban university downloaded StreetBit on their phones and participated in a crossover design study whereby the app was inactive for 3 weeks (baseline behavior phase), actively provided alerts for 3 weeks (intervention phase), and then was inactive again for 4 weeks (post-intervention phase). User distraction while crossing the intersection was collected electronically for a total of 34,923 street-crossing events throughout the 10-week study. ResultsIn crude (unadjusted) models, participant distraction was similar across all phases of the research; this result was maintained after adjusting for potential covariates as well as after conducting a sensitivity analysis limited to data from only week 3 of each study intervention phase. In a model stratified by phone/warning type and baseline distraction rates, Android phone users who received a warning that blocked the full screen and had a high baseline distraction rate (≥75% distracted crossings) had a 64% decreased odds of distraction during the alert phase (OR 0.36, 95% CI 0.25–0.51) and a 52% decreased odds of distraction during the post-intervention phase (OR 0.48, 95% CI 0.25–0.94). Users reported positive impressions about the StreetBit app in a post-intervention survey. DiscussionStreetBit, an innovative app designed to prevent distracted pedestrian behavior through a mostly-passive primary prevention strategy relying on intrusive reminders, proved effective among smartphone users who received a warning blocking the full screen and who were frequently distracted at baseline, but not among other users. The results appear to reflect the confluence of two influencing factors. First, due to software development limitations, visually-distracted Android users received a highly intrusive app warning that blocked their smartphone screen whereas iOS users received a less intrusive banner notification blocking a small upper portion of the screen. Second, most users were curious to see if the app was functioning properly, creating artificially-inflated estimates of distraction as users purposefully watched their phones when crossing. Thus, our results indicate promise for StreetBit as an effective intervention and warrant continued software development and empirical testing.

Failure Analysis of Static Analysis Software Module Based on Big Data Tendency Prediction

With the continuous development of software, it is inevitable that there will be various unpredictable problems in computer software or programs that will damage the normal operation of the software. In the paper, static analysis software is taken as the research object, the errors or failures caused by the potential defects of the software modules are analyzed, and a software analysis method based on big data tendency prediction is proposed to use the software defects of the stacked noise reduction sparse analyzer to predict. This method can learn features from original defect data, directly and efficiently extract required features of all levels from software defect data by setting different number of hidden layers, sparse regularization parameters, and noise ratio, and then classify and predict the extracted features by combining with big data. Through experimental tests, the performance of the presented method is better than that of the comparison method in correct rate, accuracy rate, recall rate, F1-measurement, AUC value, and running time, which proves that the research results in this paper have more accurate failure prediction effect and can timely eliminate software failures.

Quality Maintenance and Monitoring using Azure CI pipeline and .Net Technologies

&lt;p&gt;The paper is about how the application is maintained and monitored using Azure CI pipeline. Maintaining and monitoring the quality of the software plays an important role in company’s growth and performance. This is achieved using DevOps. Few years back agile methodology was playing a major role in the industry, software were deployed in monthly, quarterly or annual basis, which is time consuming. However, now industries are moving towards DevOps methodology where in the software deployed multiple times a day. This methodology provides the organization to constantly and reliably add new features and automatically deploy them across various platforms or environment in order to gain high performance and quality assurance products. Continuous integration and Continuous delivery/ Continuous deployment are the pillars of DevOps. Continuous integration, Continuous delivery and Continuous deployment are the continuous software development practices of industry. By automating the build, test and deployment of software, CI/CD bridges the space between development and operation teams. This paper also concentrates on how the Test Driven Development features of .Net technologies supports the quality maintenance and monitoring of the application.&lt;/p&gt;

An Analysis of the Errors in the Auto-Generated Captions of University Commencement Speeches on YouTube

Auto-generated captions on YouTube have proven useful in helping viewers better understand the words being spoken. However, at times they fail to contain accurate captions. In these cases, they lead to confusion. The aim of this paper is to identify and analyze errors in the auto-generated captions of 20 commencement speeches on YouTube. These speeches were presented over a period of 12 years by speakers from different walks of life. The researchers selected ten male and ten female icons. Only the first 10 minutes of the speeches were utilized for this investigation. All the captioned errors were collected and analyzed. Upon completion of the analysis, it was discovered that the frequency of errors in each speech ranged between 10 and 46 cases, with an average of one error occurring about every 26 seconds. Among the different error categories, nouns record the highest number with 144 cases (31.3%). The second is verbs with 93 cases (20.2%), then prepositions with 37 cases (8.1%). Among the four subcategories, namely omission, addition, substitution, and word order, substitution recorded the highest amount of errors with 357 cases (77.6%). Furthermore, the errors were classified into two major groups. The first, involving function words, appeared in 169 cases (36.7%). The second, involving content words, appeared in 291 cases (63.3%). The results of this research suggest that a continuous development of the voice recognition software that automatically generates captions is necessary for more efficient and accurate data that will help viewers and listeners better comprehend the video contents.

Prediksi dan Analisis Data Gempa Bumi di Provinsi Bengkulu dengan Metode Artificial Neural Network

Many things that have experienced technological advancements are one of which is the continued development of software. One example of software development today is the Matlab software. Matlab is currently able to be used to predict and analyze an existing problem. A lot of research has been done with the use of matlab software. Artificial Neural Network (ANN) is one of the command languages ​​used in the matlab program which is used to predict the data entered. Besides that, Bengkulu Province is one of the earthquake and tsunami prone areas in Indonesia. This is because Bengkulu is in the subduction zone (collision) of active plate encounters in Indonesia Australia and Eurasia. Recording earthquake data must be recorded properly because this earthquake data will often be used, both for development and for the environment. The ANN method uses trial epoch until the smallest error is obtained. The smallest error results stopped at the Epoch 2000 trial. The Epoch 2000 trial produced the biggest error of 59.1% and the smallest error of 2.93%. The results of this study, obtained ANN is quite capable of calculating earthquake data predictions in the province of Bengkulu in 2018.

Prioritization Based Taxonomy of DevOps Security Challenges Using PROMETHEE

DevOps is a combination of collaborative and multidisciplinary efforts of an organization to control continuous delivery and updates of new software while guaranteeing their reliability and correctness. In the software industry, the implementation of DevOps (development and operations units) faces many challenges that are specifically associated with the security. The objective of this study is to identify and develop a prioritization based taxonomy of DevOps security challenges. The total of eighteen DevOps security challenges were extracted using systematic literature review approach and were further evaluated with experts using questionnaire survey study. Finally, the multi criteria decision making PROMETHEE-II approach was used to prioritize and develop the taxonomy of identified factors and their categories. The implications of PROMETHEE-II approach are novel in this research domain as it has been used successfully in various other domains e.g. medical, banking, internet techniques and management etc. The contribution of this study is not limited to develop the taxonomy based structure of DevOps security challenges, but also the proper prioritization of these challenges by introducing PROMETHEE-II approach in the research field of DevOps. The study results will assist the practitioners to remove the uncertainty and vagueness in the opinion of DevOps experts to secure DevOps implementation for better and continuous software development process.

Advanced Battery Testing

Continuous development of hardware and software has resulted in fast, accurate and customizable testing systems

Visualizing microcalcifications in lumpectomy specimens: an exploration into the clinical potential of carbon nanotube-enabled stationary digital breast tomosynthesis

Purpose: To assess the visibility of microcalcifications in images generated by a first-generation carbon-nanotube (CNT)-enabled stationary digital breast tomosynthesis (sDBT) device, using magnified 2D mammography and conventional, moving-source DBT as references for comparison. Methods: Lumpectomy specimens were imaged by magnified mammography and two 3D mammography approaches, including sDBT and moving-source DBT. The planar size of individual microcalcifications was measured in the reconstructed image stacks of sDBT and moving-source DBT and compared to the magnified mammography image. An artifact spread function (ASF) was used to assess the depth dimensions of the microcalcifications displayed through the reconstructed image stacks. Breast-imaging specialists rated their preference for one imaging modality over another when interpreting microcalcifications in the magnified mammography image and synthetic slab images from sDBT and moving-source DBT. Results: The planar size of individual microcalcifications was similar in images generated by sDBT and moving-source DBT when the sDBT projections were binned to match the pixel size used by the moving-source DBT system. However, the unique structure of sDBT allowed for a wider-angle span of projection views and operation of the detector in full-resolution mode without significantly compromising the scan time. In this configuration, the planar sizes of individual microcalcifications displayed by sDBT was more similar to magnified mammography than moving-source DBT, and the microcalcifications had a narrower ASF through depth. Readers preferred sDBT over moving-source DBT when assessing microcalcifications in synthetic slab images, although magnified mammography was rated highest overall. Conclusions: The sDBT system displayed microcalcifications as well as conventional, moving-source DBT when the effective pixel size of the detector was matched. However, with the detector in its full-resolution mode, sDBT displayed microcalcifications with greater clarity. Readers still preferred images generated by magnified mammography over both 3D mammography approaches. This finding is guiding continued hardware and software development to optimize the sDBT technology.