Nowadays, there is a mobile application for almost every need, for instance, training, games, and news applications. Almost all the companies have also their own mobile applications that facilitate the daily communication between their employees and supervisors. In this thesis, the development process of an Android application for the company Askås is created, where the employees can for example send a Flex application or sick leave application, see their registered personal information and edit them. The final result of the project is almost what Askås asked for, An Android application that facilitates their employees’ daily needs. The development process, the results, and the encountered problems are also represented in a more detailed manner in the thesis. 

Vegetable and fruit plants facilitate around 7.5 billion people around the globe, playing a crucial role in sustaining life on the planet. The rapid increase in the use of chemicals such as fungicides and bactericides to curtail plant diseases is causing negative effects on the agro-ecosystem. The high scale prevalence of diseases in crops affects the production quantity and quality. Solving the problem of early identification/diagnosis of diseases by exploiting a quick and consistent reliable method will benefit the farmers. In this context, our research work focuses on classification and identification of tomato leaf diseases using convolutional neural network (CNN) techniques. We consider four CNN architectures, namely, VGG-16, VGG-19, ResNet, and Inception V3, and use feature extraction and parameter-tuning to identify and classify tomato leaf diseases. We test the underlying models on two datasets, a laboratory-based dataset and self-collected data from the field. We observe that all architectures perform better on the laboratory-based dataset than on field-based data, with performance on various metrics showing variance in the range 10%–15%. Inception V3 is identified as the best performing algorithm on both datasets.

The software companies are using Agile methods and practices totackle challenges in the rapidly changing environments and increasingly com-plex software systems. However, companies developing cyber physical systems(CPS) are still infancy in the use of Agile methods and hesitate to adopt. Thissystematic literature review was conducted in order to analyze the current trendsof Agile methods use for CPS development. The search strategy resulted in 101papers, of which 15 were identified as primary studies relevant to our research.The results show growing trend of Agile processes and Scrum is widely usedreported for CPS development. The primary studies also exhibits a growinginterest in teaching Agile in embedded systems, CPS and other engineeringdegree programs. The reported challenges included synchronization of softwareand hardware development, software and hardware developers use differentvocabulary, lack of visibility and track of software releases and project progress.Additionally, lesson learned were extracted from the primary studies for guidingthe practitioners interested in adopting Agile for CPS development.

This paper investigates factors affecting business analytics (BA) in software and systems development projects. This is the first study to examine business analytics continuance in projects from Pakistani software professional’s perspective. The data was collected from 186 Pakistani software professionals working in software and systems development projects. The data was analyzed using partial least squares structural equation modelling techniques. Our structural model is able to explain 40% variance of BA continuance intention, 62% variance of satisfaction, 69% variance of technological compatibility, and 59% variance of perceived usefulness. Technological compatibility and perceived usefulness are the significant factors that can affect BA continuance intention in software and systems projects. Surprisingly the results show that satisfaction does not affect BA continuance intention. 

Context: The software engineering researchers and practitioners echoed the needfor investigations to better understand the engineers developing software andservices. In light of current studies, there are significant associations between thepersonalities of software engineers and their work preferences. However, limitedstudies are using psychometric measurements in software engineering.Objective: We aim to evaluate attitudes of early-stage software engineers andinvestigate link between their personalities and work preferences.Method: We collected extensive psychometric data from 303 graduate-levelstudents in Computer Science programs at four Pakistani and one Swedish universityusing Five-Factor Model. The statistical analysis investigated associations betweenvarious personality traits and work preferences.Results: The data support the existence of two clusters of software engineers, one ofwhich is more highly rated across the board. Numerous correlations exist betweenpersonality qualities and the preferred types of employment for software developers.For instance, those who exhibit greater levels of emotional stability, agreeableness,extroversion, and conscientiousness like working on technical activities on a settimetable. Similar relationships between personalities and occupational choices arealso evident in the earlier studies. More neuroticism is reported in femalerespondents than in male respondents. Higher intelligence was demonstrated bythose who worked on the“entire development process”and“technical componentsof the project.”Conclusion: When assigning project tasks to software engineers, managers might usethe statistically significant relationships that emerged from the analysis of personalityattributes. It would be beneficial to construct effective teams by taking personalityfactors like extraversion and agreeableness into consideration. The study techniquesand analytical tools we use may identify subtle relationships and reflect distinctionsacross various groups and populations, making them valuable resources for bothfuture academic research and industrial practice.

To create competitive advantages, companies are leaning towards business analytics (BA) to make data-driven decisions. Nevertheless, users acceptance and effective usage of BA is a key element for its success. Around the globe, organizations are increasingly adopting BA, however, a paucity of research on examining the drivers of BA adoption and its continuance is noticeable in the literature. This is evident in developing countries where a higher number of systems and software development projects are outsourced. This is the first study to examine BA continuance in the context of software and systems development projects from the perspective of Pakistani software professionals. The data was collected from 186 Pakistani software professionals working in software and systems development projects. The data were analyzed using partial least squares - structural equation modelling techniques. Our structural model explains 45% variance on BA continuance intention, 69% variance on technological compatibility, and 59% variance on perceived usefulness. Our results show that confirmation has a direct impact on BA continuance intention in software and systems projects. The study has both theoretical and practical implications for professionals in the field of business analytics.

In software engineering, technical debt (TD) has been widely investigated, but debt regarding social issues, people, and processes has not been explored as much. It should be noted here that we use nontechnical debt (NTD) as an umbrella term to cover social, process, and people debts. Although the number of studies on NTD in software is increasing, the majority of them are descriptive rather than rigorous, and there is no systematic development of cumulative knowledge. As a result, identifying the fundamental causes of NTD and the associated mitigation techniques in software engineering is challenging. Therefore, this study investigates the scientific evidence regarding NTD till date by conducting a systematic mapping review of software engineering research between January 2000 and October 2021. The search strategy resulted in 175 studies, 17 of which were identified as unique and relevant primary papers. The primary studies show that NTD and TD are inextricably linked. In addition, this study also captured a plethora of causes and mitigation strategies for managing NTD and thus makes four important contributions: (i) highlighting state-of-the-art NTD research; (ii) identification of the reported causes and mitigation strategies in the primary papers; and (iii) determination of opportunities for future NTD research.

A natural method to evaluate the effectiveness of a testing technique is to measure the defect detection rate when applying the created test cases. Here, real or artificial software defects can be injected into the source code of software. For a more extensive evaluation, injection of artificial defects is usually needed and can be performed via mutation testing using code mutation operators. However, to simulate complex defects arising from a misunderstanding of design specifications, mutation testing might reach its limit in some cases. In this paper, we present an open-source benchmark testbed application that employs a complement method of artificial defect injection. The application is compiled after artificial defects are injected into its source code from predefined building blocks. The majority of the functions and user interface elements are covered by creating front-end-based automated test cases that can be used in experiments.

Abstract—Smart TVs are coming to dominate the televisionmarket. This accompanied by an increase in the use of the smartTV applications (apps). Due to the increasing demand, developersneed modeling techniques to analyze these apps and assess theircomprehensiveness, completeness, and quality. In this paper, wepresent an automated strategy for generating models of smartTV apps based on a black-box reverse engineering. The strategycan be used to cumulatively construct a model for a given app byexploring the user interface in a manner consistent with the use ofa remote control device and extracting the runtime information.The strategy is based on capturing the states of the user interfaceto create a model during runtime without any knowledge ofthe internal structure of the app. We have implemented ourstrategy in a tool called EvoCreeper. The evaluation results showthat our strategy can automatically generate unique states anda comprehensive model that represents the real user interactionswith an app using a remote control device. The models thusgenerated can be used to assess the quality and completeness ofsmart TV apps in various contexts, such as the control of otherconsumer electronics in smart houses.

Smart TV application (app) is a new technological software app that can deal with smart TV devices to add more functionality and features. Despite its importance nowadays, far too little attention has been paid to present a systematic approach to test this kind of app so far. In this paper, we present a systematic model-based testing approach for smart TV app. We used our new notion of model separation to use sub-models based on the user preference instead of the exhaustive testing to generate the test cases. Based on the constructed model, we generated a set of test cases to assess the selected paths to the chosen destination in the app. We also defined new mutation operators for smart TV app to assess our testing approach. The evaluation results showed that our approach can generate more comprehensive models of smart TV apps with less time as compared to manual exploratory testing. The results also showed that our approach can generate effective test cases in term of fault detection.

The web application Testpersonbokaren is used by Nordic Medtest to book fake test persons for various actors in healthcare so that they can test and validate their environments without being dependent on real personal data. The purpose of this project is to develop a new version of Testpersonbokaren with new technologies and frameworks as the existing version has several problems and limitations. This was achieved by creating a Node.js server and developing two new web interfaces for Testpersonbokaren, where one is only used by Nordic Medtest and the other is used by Sweden's regions, municipalities and private actors. Both web interfaces are developed with the React framework. The new Testpersonbokaren has a more modern look and more functionality than the old one. Unlike the old version, the new one has good documentation, which makes the further development of the web application easier. The new Testpersonbokaren is ready to be put into operation at Nordic Medtest.

Ethernet Virtual Private Network (EVPN) is an emerging technology that addresses the networking challenges presented by geo-distributed Data Centers (DCs). One of the major advantages of EVPN over legacy layer 2 VPN solutions is providing All-Active (A-A) mode of operation so that the traffic can truly be multi-homed on Provider Edge (PE) routers. However, A-A mode of operation introduces new challenges. In the case where the Customer Edge (CE) router is multi-homed to one or more PE routers, it is necessary that only one of the PE routers should forward Broadcast, Unknown unicast, and Multicast (BUM) traffic into the DC. The PE router that assumes the primary role for forwarding BUM traffic to the CE device is called the Designated Forwarder (DF). The proposed solution to select the DF in the EVPN standard is based on a distributed algorithm which has a number of drawbacks such as unfairness and intermittent behavior. In this paper, we introduce a Software-Defined Networking (SDN) based architecture for EVPN support, where the SDN controller interacts with EVPN control plane. We demonstrate how our solution mitigates existing problems for DF selection which leads to improved EVPN performance.

Most industrial processes in real-world manufacturing applications are characterized by the scalability property, which requires an automated strategy to self-adapt machine learning (ML) software systems to the new conditions. In this paper, we investigate an Electroslag Remelting (ESR) use case process from the Uddeholms AB steel company. The use case involves predicting the minimum pressure value for a vacuum pumping event. Taking into account the long time required to collect new records and efficiently integrate the new machines with the built ML software system. Additionally, to accommodate the changes and satisfy the non-functional requirement of the software system, namely adaptability, we propose an automated and adaptive approach based on a drift handling technique called importance weighting. The aim is to address the problem of adding a new furnace to production and enable the adaptability attribute of the ML software. The overall results demonstrate the improvements in ML software performance achieved by implementing the proposed approach over the classical non-adaptive approach. 

Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world applications generate huge volumes of data at speeds. These data streams require analysis and preprocessing before being permanently stored or used in a learning task. Therefore, significant attention has been paid to the systematic management and construction of high-quality datasets. Nevertheless, managing voluminous and high-velocity data streams is usually performed manually (i.e. offline), making it an impractical strategy in production environments. To address this challenge, DataOps has emerged to achieve life-cycle automation of data processes using DevOps principles. However, determining the data quality based on a fitness scale constitutes a complex task within the framework of DataOps. This paper presents a novel Data Quality Scoring Operations (DQSOps) framework that yields a quality score for production data in DataOps workflows. The framework incorporates two scoring approaches, an ML prediction-based approach that predicts the data quality score and a standard-based approach that periodically produces the ground-truth scores based on assessing several data quality dimensions. We deploy the DQSOps framework in a real-world industrial use case. The results show that DQSOps achieves significant computational speedup rates compared to the conventional approach of data quality scoring while maintaining high prediction performance.

Rapporten handlar om framställningen av ett prototypverktyg för konsultfirman Logica. Marknaden har idag ett stort behov av ett prototypverktyg och det är en lösning på det problemet som vi utvecklat. Utvecklingsarbetet är utfört i Java och har resulterat i ett program som kan rita upp prototyper och ett som kan visa och köra prototypprojekt. Projektets syfte har varit att effektivisera Logicas arbetssätt och tillgodose ett behov av en mjukvara som är avsett för att skapa och hantera prototyper av mjukvarusystem. Därutöver har projektet syftat till att erbjuda Logicas kunder mervärde då de får en körbar prototyp av det system de överväger att köpa.

Reflexion Modelling is considered one of the more successful approaches to architecture reconciliation. Empirical studies strongly suggest that professional developers involved in real-life industrial projects find the information provided by variants of this approach useful and insightful, but the degree to which it resolves architecture conformance issues is still unclear.

Objective

This paper aims to assess the level of architecture conformance achieved by professional architects using Reflexion Modelling, and to determine how the approach could be extended to improve its suitability for this task.

Context

Executing various sequences of system functions in a system under test represents one of the primary techniques in software testing. The natural method for creating effective, consistent and efficient test sequences is to model the system under test and employ an algorithm to generate tests that satisfy a defined test coverage criterion. Several criteria for preferred test set properties can be defined. In addition, to optimize the test set from an economic viewpoint, the priorities of the various parts of the system model under test must be defined.

Objective

Using this prioritization, the test cases exercise the high-priority parts of the system under test by more path combinations than those with low priority (this prioritization can be combined with the test coverage criterion that determines how many path combinations of the individual parts of the system are tested). Evidence from the literature and our observations confirm that finding a universal algorithm that produces a test set with preferred properties for all test coverage criteria is a challenging task. Moreover, for different individual problem instances, different algorithms provide results with the best value of a preferred property. In this paper, we present a portfolio-based strategy to perform the best test selection.

Method

The proposed strategy first employs a set of current algorithms to generate test sets; then, a preferred property of each test set is assessed in terms of the selected criterion, and finally, the test set with the best value of a preferred property is chosen.

Results

The experimental results confirm the validity and usefulness of this strategy. For individual instances of 50 system under test models, different algorithms provided results having the best preferred property value; these results varied by the required test coverage level, the size of the priority parts of the model, and the selected test set preferred property criteria.

Conclusion

In addition to the used algorithms, the proposed strategy can be used to assess the optimality of different path-based testing algorithms and choose a suitable algorithm for the testing.

As the popularity of Smart Televisions (TVs) and interactive Smart TV applications (apps) has recently grown, the usability of these apps has become an important quality characteristic. Previous studies examined Smart TV apps from a usability perspective. However, these methods are mainly manual, and the potential of automated model-based testing methods for usability testing purposes has not yet been fully explored. In this paper, we propose an approach to test the usability of Smart TV apps based on the automated generation of a Smart TV user interaction model from an existing app by a specialized automated crawler. By means of this model, defined user tasks in the Smart TV app can be evaluated automatically in terms of their feasibility and estimated user effort, which reflects the usability of the analyzed app. This analysis can be applied in the context of regular users and users with various specific needs. The findings from this model-based automated analysis approach can be used to optimize the user interface of a Smart TV app to increase its usability, accessibility, and quality.

There is often a scarcity of training data for machine learning (ML) classification and regression models in industrial production, especially for time-consuming or sparsely run manufacturing processes. Traditionally, a majority of the limited ground-truth data is used for training, while a handful of samples are left for testing. In that case, the number of test samples is inadequate to properly evaluate the robustness of the ML models under test (i.e., the system under test) for classification and regression. Furthermore, the output of these ML models may be inaccurate or even fail if the input data differ from the expected. This is the case for ML models used in the Electroslag Remelting (ESR) process in the refined steel industry to predict the pressure in a vacuum chamber. A vacuum pumping event that occurs once a workday generates a few hundred samples in a year of pumping for training and testing. In the absence of adequate training and test samples, this paper first presents a method to generate a fresh set of augmented samples based on vacuum pumping principles. Based on the generated augmented samples, three test scenarios and one test oracle are presented to assess the robustness of an ML model used for production on an industrial scale. Experiments are conducted with real industrial production data obtained from Uddeholms AB steel company. The evaluations indicate that Ensemble and Neural Network are the most robust when trained on augmented data using the proposed testing strategy. The evaluation also demonstrates the proposed method's effectiveness in checking and improving ML algorithms' robustness in such situations. The work improves software testing's state-of-the-art robustness testing in similar settings. Finally, the paper presents an MLOps implementation of the proposed approach for real-time ML model prediction and action on the edge node and automated continuous delivery of ML software from the cloud. 

Network Function Virtualization (NFV) is an emerging network architecture to increase flexibility and agility within operator's networks by placing virtualized services on demand in Cloud data centers (CDCs). One of the main challenges for the NFV environment is how to minimize network latency in the rapidly changing network environments. Although many researchers have already studied in the field of Virtual Machine (VM) migration and Virtual Network Function (VNF) placement for efficient resource management in CDCs, VNF migration problem for low network latency among VNFs has not been studied yet to the best of our knowledge. To address this issue in this article, we i) formulate the VNF migration problem and ii) develop a novel VNF migration algorithm called VNF Real-time Migration (VNF-RM) for lower network latency in dynamically changing resource availability. As a result of experiments, the effectiveness of our algorithm is demonstrated by reducing network latency by up to 70.90% after latency-aware VNF migrations.

Spreadsheets are commonly used to inform decision making across many business sectors, despite the fact that research performed in the financial sector has shown that they are quite error-prone. However, few studies have investigated spreadsheet errors and their impact in other domains, like the healthcare sector. This article derives a lifecycle-stage classification scheme of spreadsheet error types based on an aggregation of, and extension of, existing classifications. Based on these classifications, a case study is then presented, performed to investigate the prevalence of these spreadsheet error types in an Irish healthcare setting. Results reveal that more than 90% of the spreadsheets studied contained ` bottom-line' errors and the average cell-error rate was 13%. There was also a correlation between increased perceived impact of the spreadsheets and the number of errors identified. Recommendations from this research include providing spreadsheet training and guidelines for developers and users, and systematically managing and auditing spreadsheet development and use.

A mapping between a system's implementation and its software architecture is mandatory in many architecture consistency checking techniques. Creating such a mapping manually is a non-trivial task for most complex software systems. Machine learning-based text classification may be an highly effective tool for automating this task. How to make use of this tool most effectively has not been thoroughly investigated yet.

This article presents a comparative analysis of three classifiers applied to map the implementations of five open-source systems to their architectures. The performance of the classifiers is evaluated for different extraction and preprocessing settings as well as different training set sizes.

The results suggest that Logical Regression and Support Vector Machines both outperform Naive Bayes unless information about coarse-grained implementation structures cannot be exploited. Moreover, initial manual mappings of more than 15% of all source code files, or 10 files per module, do not seem to lead to a significantly better classification.

Context: Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored. Objective: We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners’ understanding and managing of requirements engineering debt (RED). Method: We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory. Results: We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results. Conclusions: The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area. 

Agile ways of working are nowadays used in many software development departments even in larger organizations. When scaling up agile ways of working, new practices for coordinating teams become necessary. Instead of inventing practices on their own, many organizations are implementing the Scaled Agile Framework but the impacts on team performance due to these practices are not much studied. Data was obtained by means of a survey questionnaire that was answered by 111 team members from two organizations, one from the automotive industry and one of the major Swedish banks. The study suggests that efficient inter-team coordination does not have a positive relationship to team performance. But, as shown in several other contexts, a high level of psychological safety has a significant positive impact on team performance in a large-scale agile software development setting. © 2018 CEUR-WS. All rights reserved.

Large-scale agile frameworks suggest several practices for coordination between teams in software development projects. These prescribed coordination practices are often described as how to be performed in detail but without explaining the intended impacts. At the same time, agile values emphasise the need for tailoring practices that are not helpful. This study examines how three agile coordination practices influence accountability, predictability, and common understanding, which are important conditions for coordination. A qualitative research strategy was used to investigate three case organisations that implemented the scaled agile framework (SAFe): one bank, one government agency, and one department within the automotive industry. The empirical data consists of 28 semi-structured interviews and observations. The findings show the differing impacts of three investigated agile coordination practices and how the tailoring of practices affects coordination. 

This thesis provides insight in the further development of an application for managing speed skating events, based on the previous developed application by a group of students in fall 2010. The application was implemented for the Karlstad Speed Skating Club, which before used a program from the 90's. After finishing the implementation, all basic functionality requested by the Karlstad Speed Skating Club can be used for managing speed skating events.

The first part of the thesis presents a short summary of our work, comparing what we wanted to implement and what we achieved. A short background on speed skating, the old system, developed by a group of students, and the project requirements are presented. An introduction to the used tools, languages, techniques and development processes is given along with details concerning the implementation of the requirements. Finally, the design of the new user interface is explained and the results are presented, containing an evaluation of the implementation, occured problems and what could be added in the future.

Workflow engines are frequently used in the service-oriented and cloud computing domains. Since engines have significant impact on the quality of service provided by hosted applications, it is desirable to compare and select the most appropriate engine for a given task. To enable such a comparison, approaches for benchmarking workflow engines have emerged. Although these approaches deal with different quality properties, such as performance or standard conformance, they face many reoccurring problems during the design and implementation phase, which they solve in similar ways. In this paper, we describe such common solutions to reoccurring problems in the area of workflow engine benchmarking as patterns. Our aim is to present pattern candidates that help benchmark authors to design and implement proper and valid workflow engine benchmarks and benchmarking tools

Workflow engines are frequently used in the domains of business process management, service orchestration, and cloud computing, where they serve as middleware platforms for integrated business applications. Engines have a significant impact on the quality of service provided by hosted applications. Therefore, it is desirable to compare them and to select the most appropriate engine for a given task. To enable such a comparison, approaches for benchmarking workflow engines have emerged. Although these approaches deal with different quality attributes, i.e., performance or standard conformance, they face many reoccurring design and implementation problems, which have been solved in similar ways. In this paper, we present a pattern language that captures such common solutions to reoccurring problems (e.g., from test identification, benchmarking procedure validation, automatic engine interaction, and workflow execution observation) in the area of workflow engine conformance and performance benchmarking. Our aim is to help future benchmark authors with the pattern language presented in this paper to benefit from our experience with the design and implementation of workflow engine benchmarks and benchmarking tools

It is a long-standing debate, whether software that is developed as open source is generally of higher quality than proprietary software. Although the open source community has grown immensely during the last decade, there is still no clear answer. Service-oriented software and middleware tends to rely on highly complex and interrelated standards and frameworks. Thus, it is questionable if small and loosely coupled teams, as typical in open source software development, can compete with major vendors. Here, we focus on a central part of service-oriented software systems, i.e., process engines for service orchestration, and compare open source and proprietary solutions. We use the Web Services Business Process Execution Language (BPEL) and compare standard conformance and its impact on language expressiveness in terms of workflow pattern support of eight engines. The results show that, although the top open source engines are on par with their proprietary counterparts, in general proprietary engines perform better