| 1. |
- Lehman, Joel, et al.
(författare)
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The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
- 2020
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Ingår i: Artificial Life. - : MIT Press - Journals. - 1530-9185 .- 1064-5462. ; 26:2, s. 274-306
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Tidskriftsartikel (refereegranskat)abstract
- Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.
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| 2. |
- Pepic, I., et al.
(författare)
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Early detection of sepsis using artificial intelligence : a scoping review protocol
- 2021
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Ingår i: Systematic Reviews. - : Springer Nature. - 2046-4053. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence.Methods: The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 31 December 2020 will be taken into consideration, and foreign language publications will not be considered, i.e., only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library, and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly, and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain.Ethics and dissemination: The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.
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| 3. |
- Pernstal, J., et al.
(författare)
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FLEX-RCA: a lean-based method for root cause analysis in software process improvement
- 2019
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Ingår i: Software Quality Journal. - : Springer Science and Business Media LLC. - 1573-1367 .- 0963-9314. ; 27:1, s. 389-428
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Tidskriftsartikel (refereegranskat)abstract
- Software process improvement (SPI) is an instrument to increase the productivity of, and the quality of work, in software organizations. However, a majority of SPI frameworks are too extensive or provide guidance and potential improvement areas at a high level, indicating only the symptoms, not the causes. Motivated by the industrial need of two Swedish automotive companies to systematically uncover the underlying root causes of high-level improvement issues identified in an SPI project-assessing inter-departmental interactions in large-scale software systems development-this paper advances a root cause analysis (RCA) method building on Lean Six Sigma, called Flex-RCA. Flex-RCA is used to delve deeper into challenges identified to find root causes as a part of the evaluation and subsequent improvement activities. We also demonstrate and evaluate Flex-RCA's industrial applicability in a case study. An overall conclusion is that the use of Flex-RCA was successful, showing that it had the desired effect of both producing a broad base of causes on a high level and, more importantly, enabling an exploration of the underlying root causes.
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| 4. |
- Pernstål, Joakim, et al.
(författare)
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Communication Problems in Software Development : A Model and Its Industrial Application
- 2019
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Ingår i: International journal of software engineering and knowledge engineering. - : World Scientific Publishing Co. Pte Ltd. - 0218-1940. ; 29:10, s. 1497-1538
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Tidskriftsartikel (refereegranskat)abstract
- Attaining effective communication within and across organizational units is among the most critical challenges for success in software development organizations. This paper presents a novel model, supporting analysis of problems in inter-departmental communication events. The model was developed and designed based on industrial needs emphasizing flexibility, applicability and scalability. The model covers central communication aspects in order to provide a useful approximation of communication problems rather than in-depth modeling on message-by message basis. Other event-specific information, such as costs, can then be attached to enrich analysis and understanding. To exemplify and evaluate the model and collect feedback from industry, it was applied to 16 events at a Swedish automotive manufacturer where communication between two departments had broken down during development of software-intensive systems. The evaluation showed that the model helped structure and conduct systematic data collection and analysis of dysfunctional communication patterns. We found that insufficient understanding of the matters being communicated was prevalent, but also more specifically, requirements were insufficiently balanced, detailed and specified over the full system development cycle. Besides, the long-term cost for the company was analyzed in depth for each event, yielding a total estimated cost for the analyzed communication events of 11.2MUS$. © 2019 World Scientific Publishing Company.
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| 5. |
- Afzal, Wasif, et al.
(författare)
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Genetic programming for cross-release fault count predictions in large and complex software projects
- 2010
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Ingår i: Evolutionary Computation and Optimization Algorithms in Software Engineering. - : IGI Global, Hershey, USA. - 9781615208098
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Bokkapitel (refereegranskat)abstract
- Software fault prediction can play an important role in ensuring software quality through efficient resource allocation. This could, in turn, reduce the potentially high consequential costs due to faults. Predicting faults might be even more important with the emergence of short-timed and multiple software releases aimed at quick delivery of functionality. Previous research in software fault prediction has indicated that there is a need i) to improve the validity of results by having comparisons among number of data sets from a variety of software, ii) to use appropriate model evaluation measures and iii) to use statistical testing procedures. Moreover, cross-release prediction of faults has not yet achieved sufficient attention in the literature. In an attempt to address these concerns, this paper compares the quantitative and qualitative attributes of 7 traditional and machine-learning techniques for modeling the cross-release prediction of fault count data. The comparison is done using extensive data sets gathered from a total of 7 multi-release open-source and industrial software projects. These software projects together have several years of development and are from diverse application areas, ranging from a web browser to a robotic controller software. Our quantitative analysis suggests that genetic programming (GP) tends to have better consistency in terms of goodness of fit and accuracy across majority of data sets. It also has comparatively less model bias. Qualitatively, ease of configuration and complexity are less strong points for GP even though it shows generality and gives transparent models. Artificial neural networks did not perform as well as expected while linear regression gave average predictions in terms of goodness of fit and accuracy. Support vector machine regression and traditional software reliability growth models performed below average on most of the quantitative evaluation criteria while remained on average for most of the qualitative measures.
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| 6. |
- Ahmad, E., et al.
(författare)
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Assessment and support for software capstone projects at the undergraduate level: A survey and rubrics
- 2011
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Ingår i: Frontiers of Information Technology (FIT), 2011, Islamabad, 19-21 Dec. 2011. - Islamabad. - 9780769546254 ; , s. 25 - 32
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Konferensbidrag (refereegranskat)abstract
- Software engineering and computer science students conduct a capstone project during the final year of their degree programs. These projects are essential in validating that students have gained required knowledge and they can synthesize and use that knowledge to solve real world problems. However, the external requirements on educational programs often do not provide detailed guidelines for how to conduct or support these capstone projects, which may lead to variations among universities. This paper presents the results from a survey conducted at 19 different Pakistani universities of the current management practices and assessment criteria used for the capstone project courses at Undergraduate level. Based upon the results of this survey and similar work on Master Thesis capstone projects in Sweden, we present assessment rubrics for software-related undergraduate capstone projects. We also present recommendations for the continuous improvement of capstone projects.
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| 7. |
- Alégroth, Emil, 1984, et al.
(författare)
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Industrial application of visual GUI testing: Lessons learned
- 2014
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Ingår i: Continuous software engineering. - Cham : Springer International Publishing. ; , s. 127-140
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- A large body of academic knowledge has been devoted to automated software testing in order to support the software market's demands for continuous software delivery. However, most of these automated techniques approach testing from lower levels of system abstraction, e.g., component level, which limit their applicability for high-level regression testing of, for instance, system and acceptance tests, thus forcing companies to perform these test activities manually, which is considered time consuming, tedious, and error prone. In this book chapter, we present visual GUI testing (VGT), a tool driven test technique that uses image recognition in order to interact and assert the correctness of a system under test (SUT) through the bitmap graphical user interface (GUI) that is shown to the user on the computer monitor. This approach makes VGT flexible and applicable to any SUT with a GUI but also allows VGT tools to emulate end-user behavior and therefore automate previously manual system and acceptance test cases. In addition to presenting the technique itself, this chapter will also present some VGT tools and empirically identified problems with the technique and how these problems can be mitigated in practice. Finally we will discuss how VGT can be used in the context of continuous software development in order to support market demands for quicker software delivery.
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| 8. |
- Alégroth, Emil, 1984, et al.
(författare)
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Maintenance of automated test suites in industry: An empirical study on Visual GUI Testing
- 2016
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Ingår i: Information and Software Technology. - : Elsevier BV. - 0950-5849 .- 1873-6025. ; 73, s. 66-80
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Tidskriftsartikel (refereegranskat)abstract
- Context: Verification and validation (V&V) activities make up 20-50% of the total development costs of a software system in practice. Test automation is proposed to lower these V&V costs but available research only provides limited empirical data from industrial practice about the maintenance costs of automated tests and what factors affect these costs. In particular, these costs and factors are unknown for automated GUI-based testing. Objective: This paper addresses this lack of knowledge through analysis of the costs and factors associated with the maintenance of automated GUI-based tests in industrial practice. Method: An empirical study at two companies, Siemens and Saab, is reported where interviews about, and empirical work with, Visual GUI Testing is performed to acquire data about the technique's maintenance costs and feasibility. Results: 13 factors are observed that affect maintenance, e.g. tester knowledge/experience and test case complexity. Further, statistical analysis shows that developing new test scripts is costlier than maintenance but also that frequent maintenance is less costly than infrequent, big bang maintenance. In addition a cost model, based on previous work, is presented that estimates the time to positive return on investment (ROI) of test automation compared to manual testing. Conclusions: It is concluded that test automation can lower overall software development costs of a project while also having positive effects on software quality. However, maintenance costs can still be considerable and the less time a company currently spends on manual testing, the more time is required before positive, economic, ROI is reached after automation.
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| 9. |
- Alégroth, Emil, 1984, et al.
(författare)
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Special issue on new generations of UI testing
- 2021
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Ingår i: Software Testing Verification and Reliability. - : Wiley. - 0960-0833 .- 1099-1689. ; 31:3
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 10. |
- Alégroth, Emil, 1984, et al.
(författare)
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Transitioning manual system test suites to automated testing: An industrial case study
- 2013
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Ingår i: Proceedings - IEEE 6th International Conference on Software Testing, Verification and Validation, ICST 2013. - : IEEE. - 9780769549682 ; , s. 56-65
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Konferensbidrag (refereegranskat)abstract
- Visual GUI testing (VGT) is an emerging technique that provides software companies with the capability to automate previously time-consuming, tedious, and fault prone manual system and acceptance tests. Previous work on VGT has shown that the technique is industrially applicable, but has not addressed the real-world applicability of the technique when used by practitioners on industrial grade systems. This paper presents a case study performed during an industrial project with the goal to transition from manual to automated system testing using VGT. Results of the study show that the VGT transition was successful and that VGT could be applied in the industrial context when performed by practitioners but that there were several problems that first had to be solved, e.g. testing of a distributed system, tool volatility. These problems and solutions have been presented together with qualitative, and quantitative, data about the benefits of the technique compared to manual testing, e.g. greatly improved execution speed, feasible transition and maintenance costs, improved bug finding ability. The study thereby provides valuable, and previously missing, contributions about VGT to both practitioners and researchers. © 2013 IEEE.
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