| 1. |
- Alahdab, Mohannad, et al.
(author)
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Empirical Analysis of Hidden Technical Debt Patterns in Machine Learning Software
- 2019
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11915 LNCS, s. 195-202
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Conference paper (peer-reviewed)abstract
- Context/Background Machine Learning (ML) software has special ability for increasing technical debt due to ML-specific issues besides having all the problems of regular code. The term “Hidden Technical Debt” (HTD) was coined by Sculley et al. to address maintainability issues in ML software as an analogy to technical debt in traditional software. Goal The aim of this paper is to empirically analyse how HTD patterns emerge during the early development phase of ML software, namely the prototyping phase. Method Therefore, we conducted a case study with subject systems as ML models planned to be integrated into the software system owned by Västtrafik, the public transportation agency in the west area of Sweden. Results During our case study, we could detect HTD patterns, which have the potential to emerge in ML prototypes, except for “Legacy Features”, “Correlated features”, and “Plain Old Data Type Smell”. Conclusion Preliminary results indicate that emergence of significant amount of HTD patterns can occur during prototyping phase. However, generalizability of our results require analyses of further ML systems from various domains.
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| 2. |
- Calikli, Gul, et al.
(author)
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Effects of automated competency evaluation on software engineers' emotions and motivation: A case study
- 2018
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In: SEmotion '18 Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering. - New York, NY, USA : ACM. - 0270-5257. - 9781450357517
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Conference paper (peer-reviewed)abstract
- © 2018 ACM. Software development consulting companies must be able to select the best suited developers for their clients. A method of doing this is through competence evaluation. Sigma IT Consulting uses manual methods consisting of heavy documentation for employees to fill in their competence. Problems such as data inconsistencies in documentation of competency might cause difficulties for managers while making decisions to assign right developer to the right job. Such difficulties may lead to frustration in managers and negatively affect their decision-making process. Similarly, developers might feel themselves under pressure always having to fill in the competency documents whenever the manager makes requests among all the tasks the developers are busy with and feeling under pressure might have negative effects on developers' performance. Researchers have shown that negative emotions lead to poor software development performance, while positive emotions improve developers' performance. Competency evaluation is an integral part of the daily routine at Sigma IT Consulting. Therefore, negative effects of competency sheets on developers and managers cannot be tolerated. In this case study, having investigated how competency is evaluated at Sigma IT and what employees think about competency evaluation in general, we implemented a web-based competency evaluation platform. When supplemented with qualitative data, the results of the Self-Assessment Manikin (SAM) and Intrinsic Motivation Inventory (IMI) we conducted show that automation of competency evaluation as a web-based platform has positive effects on developers' and managers' emotions and motivations. Interviews we conducted with developers and managers also include their positive thoughts about automation of the competency evaluation.
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| 3. |
- Calikli, Gul, et al.
(author)
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Measure early and decide fast: Transforming quality management and measurement to continuous deployment
- 2018
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In: ACM International Conference Proceeding Series. - New York, NY, USA : ACM.
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Conference paper (peer-reviewed)abstract
- © 2018 Association for Computing Machinery. Continuous deployment has become software companies' inevitable response to the economic pressures of the market. At the same time, software quality is crucial in order to meet customers' expectations and hence succeed in the market. Therefore, current quality management processes require transformation in order to keep up with the fast pace of the market while at the same time meeting customers' expectations. In order to figure out how the current quality management process should be transformed to keep up with the fast pace of the market while ensuring both product quality and continuous deployment, we conducted a qualitative study at a large infrastructure provider company. During the interviews we conducted with the quality manager, developer and test architect, we used a metrics portfolio consisting of 59 candidate metrics that can be used in the transformed quality management process. Our findings show that, out of these candidate metrics, 9 metrics should be used in the internal quality measurement dashboard for quality check at the end of the software development life-cycle (SDLC) before the software is released to customer site, while 3 metrics should be used by quality manager to monitor earlier phases of SDLC and 5 metrics should also be delegated to earlier phases of SDLC but without the involvement of the quality manager. To summarize, our study support the claim that quality managers should not be only gatekeepers, but also proactive controllers of quality by monitoring earlier phases of the SDLC.
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| 4. |
- Kasauli, Rashidah, et al.
(author)
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Safety-Critical Systems and Agile Development: A Mapping Study
- 2018
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In: 44TH EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2018). - 1089-6503. - 9781538673836
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Conference paper (peer-reviewed)abstract
- Several studies report that the use of model-centric methods in the automotive domain is widespread and offers several benefits. However, existing work indicates that few modelling frameworks explicitly include requirements engineering (RE), and that natural language descriptions are still the status quo in RE. Therefore, we aim to increase the understanding of current and potential future use of models in RE, with respect to the automotive domain. In this paper, we report our findings from a multiple-case study with two automotive companies, collecting interview data from 14 practitioners. Our results show that models are used for a variety of different purposes during RE in the automotive domain, e.g., to improve communication and to handle complexity. However, these models are often used in an unsystematic fashion and restricted to few experts. A more widespread use of models is prevented by various challenges, most of which align with existing work on model use in a general sense. Furthermore, our results indicate that there are many potential benefits associated with future use of models during RE. Interestingly, existing research does not align well with several of the proposed use cases, e.g., restricting the use of models to informal notations for communication purposes. Based on our findings, we recommend a stronger focus on informal modelling and on using models for multi-disciplinary environments. Additionally, we see the need for future work in the area of model use, i.e., information extraction from models by non-expert modellers.
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| 5. |
- Kasauli, Rashida, 1984, et al.
(author)
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Safety-Critical Systems and Agile Development: A Mapping Study
- 2018
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In: Proceedings - 44th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2018. ; , s. 470-477
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Conference paper (peer-reviewed)abstract
- In the last decades, agile methods had a huge impact on how software is developed. In many cases, this has led to significant benefits, such as quality and speed of software deliv- eries to customers. However, safety-critical systems have widely been dismissed from benefiting from agile methods. Products that include safety critical aspects are therefore faced with a situation in which the development of safety-critical parts can significantly limit the potential speed-up through agile methods, for the full product, but also in the non-safety critical parts. For such products, the ability to develop safety-critical software in an agile way will generate a competitive advantage. In order to enable future research in this important area, we present in this paper a mapping of the current state of praxis based on a mixed method approach. Starting from a workshop with experts from six large Swedish product development companies we develop a lens for our analysis. We then present a systematic mapping study on safety-critical systems and agile development through this lens in order to map potential benefits, challenges, and solution candidates for guiding future research.
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| 6. |
- Kruger, J., et al.
(author)
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Effects of Explicit Feature Traceability on Program Comprehension
- 2019
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In: ESEC/FSE 2019. Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. - New York, NY, USA : IEEE. - 9781450355728
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Conference paper (peer-reviewed)abstract
- Developers spend a substantial amount of their time with program comprehension. To improve their comprehension and refresh their memory, developers need to communicate with other developers, read the documentation, and analyze the source code. Many studies show that developers focus primarily on the source code and that small improvements can have a strong impact. As such, it is crucial to bring the code itself into a more comprehensible form. A particular technique for this purpose are explicit feature traces to easily identify a program's functionalities. To improve our empirical understanding about the effects of feature traces, we report an online experiment with 49 professional software developers. We studied the impact of explicit feature traces, namely annotations and decomposition, on program comprehension and compared them to the same code without traces. Besides this experiment, we also asked our participants about their opinions in order to combine quantitative and qualitative data. Our results indicate that, as opposed to purely object-oriented code: (1) annotations can have positive effects on program comprehension; (2) decomposition can have a negative impact on bug localization; and (3) our participants perceive both techniques as beneficial. Moreover, none of the three code versions yields significant improvements on task completion time. Overall, our results indicate that lightweight traceability, such as using annotations, provides immediate benefits to developers during software development and maintenance without extensive training or tooling; and can improve current industrial practices that rely on heavyweight traceability tools (e.g., DOORS) and retroactive fulfillment of standards (e.g., ISO-26262, DO-178B).
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| 7. |
- Mahmood, Wardah, 1992, et al.
(author)
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Virtual Platform: Effective and Seamless Variability Management for Software Systems
- 2024
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In: IEEE Transactions on Software Engineering. - 0098-5589 .- 1939-3520. ; In Press
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Journal article (peer-reviewed)abstract
- Customization is a general trend in software engineering, demanding systems that support variable stakeholder requirements. Two opposing strategies are commonly used to create variants: software clone & own and software configuration with an integrated platform. Organizations often start with the former, which is cheap and agile, but does not scale. The latter scales by establishing an integrated platform that shares software assets between variants, but requires high up-front investments or risky migration processes. So, could we have a method that allows an easy transition or even combine the benefits of both strategies? We propose a method and tool that supports a truly incremental development of variant-rich systems, exploiting a spectrum between the opposing strategies. We design, formalize, and prototype a variability-management framework: the virtual platform. Virtual platform bridges clone & own and platform-oriented development. Relying on programming-language independent conceptual structures representing software assets, it offers operators for engineering and evolving a system, comprising: traditional, asset-oriented operators and novel, feature-oriented operators for incrementally adopting concepts of an integrated platform. The operators record meta-data that is exploited by other operators to support the transition. Among others, they eliminate expensive feature-location effort or the need to trace clones. A cost-and-benefit analysis of using the virtual platform to simulate the development of a real-world variant-rich system shows that it leads to benefits in terms of saved effort and time for clone detection and feature location. Furthermore, we present a user study indicating that the virtual platform effectively supports exploratory and hands-on tasks, outperforming manual development concerning correctness. We also observed that participants were significantly faster when performing typical variability management tasks using the virtual platform. Furthermore, participants perceived manual development to be significantly more difficult than using the virtual platform, preferring virtual platform for all our tasks. We supplement our findings with recommendations on when to use virtual platform and on incorporating the virtual platform in practice.
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| 8. |
- Perera, Charith, et al.
(author)
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Guest Editorial Special Section on Engineering Industrial Big Data Analytics Platforms for Internet of Things
- 2018
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In: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 14:2, s. 744-747
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Journal article (other academic/artistic)abstract
- Over the last few years, a large number of Internet of Things (IoT) solutions have come to the IoT marketplace. Typically, each of these IoT solutions are designed to perform a single or minimal number of tasks (primary usage). We believe a significant amount of knowledge and insights are hidden in these data silos that can be used to improve our lives; such data include our behaviors, habits, preferences, life patterns, and resource consumption. To discover such knowledge, we need to acquire and analyze this data together in a large scale. To discover useful information and deriving conclusions toward supporting efficient and effective decision making, industrial IoT platform needs to support variety of different data analytics processes such as inspecting, cleaning, transforming, and modeling data, especially in big data context. IoT middleware platforms have been developed in both academic and industrial settings in order to facilitate IoT data management tasks including data analytics. However, engineering these general-purpose industrial-grade big data analytics platforms need to address many challenges. We have accepted six manuscripts out of 24 submissions for this special section (25% acceptance rate) after the strict peerreview processes. Each manuscript has been blindly reviewed by at least three external reviewers before the decisions were made. The papers are briefly summarized.
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| 9. |
- Rafiq, Y., et al.
(author)
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Learning to Share: Engineering Adaptive Decision-Support for Online Social Networks
- 2017
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In: PROCEEDINGS OF THE 2017 32ND IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE'17). - 1527-1366. - 9781538626849 ; , s. 280-285
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Book chapter (other academic/artistic)abstract
- Some online social networks (OSNs) allow users to define friendship-groups as reusable shortcuts for sharing information with multiple contacts. Posting exclusively to a friendship-group gives some privacy control, while supporting communication with (and within) this group. However, recipients of such posts may want to reuse content for their own social advantage, and can bypass existing controls by copy-pasting into a new post; this cross-posting poses privacy risks. This paper presents a learning to share approach that enables the incorporation of more nuanced privacy controls into OSNs. Specifically, we propose a reusable, adaptive software architecture that uses rigorous runtime analysis to help OSN users to make informed decisions about suitable audiences for their posts. This is achieved by supporting dynamic formation of recipient-groups that benefit social interactions while reducing privacy risks. We exemplify the use of our approach in the context of Facebook.
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| 10. |
- Spadini, D., et al.
(author)
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Primers or Reminders? The Effects of Existing Review Comments on Code Review
- 2020
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In: ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, June 2020. - New York : Association for Computing Machinery. - 9781450371216 ; , s. 1171-1182
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Conference paper (peer-reviewed)abstract
- In contemporary code review, the comments put by reviewers on a specific code change are immediately visible to the other reviewers involved. Could this visibility prime new reviewers' attention (due to the human's proneness to availability bias), thus biasing the code review outcome? In this study, we investigate this topic by conducting a controlled experiment with 85 developers who perform a code review and a psychological experiment. With the psychological experiment, we find that approximate to 70% of participants are prone to availability bias. However, when it comes to the code review, our experiment results show that participants are primed only when the existing code review comment is about a type of bug that is not normally considered; when this comment is visible, participants are more likely to find another occurrence of this type of bug. Moreover, this priming effect does not influence reviewers' likelihood of detecting other types of bugs. Our findings suggest that the current code review practice is effective because existing review comments about bugs in code changes are not negative primers, rather positive reminders for bugs that would otherwise be overlooked during code review. Data and materials: https://doi.org/10.5281/zenodo.3653856
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