131,031 research outputs found
Datan valinta projektienvälisten virheiden ennustamiseen
No full textAbstractContext: This study contributes to the understanding of the current state of cross-project defect prediction (CPDP) by investigating the topic in themes, with special focus on data approaches and covering search-based training data selection, by proposing data selection methods and investigating their impact. The empirical evidence for this work is collected through a formal systematic literature review method for the review, and from experiments on open source projects.Objective: We aim to understand and summarize the manner in which various data manipulation approaches are used in CPDP and their potential impacts on performance. Further, we aim at utilizing search-based methods to produce evolving training data sets to filter irrelevant instances from other projects before training.Method: Through a series of studies following the literature review of current state of CPDP, we propose a search-based method called genetic instance selection (GIS). We validate our initial findings by conducting the next study on a large set of data sets with multiple feature sets. We refine our design decisions using an exploratory study. Finally, we investigate an existing meta-learning approach, provide insights on its design and propose an alternative iterative data selection method.Results: The literature review reveals lower performances of CPDP in comparison with within project defect prediction (WPDP) models and provides a set of primary studies to be used as the basis for future research. Our proposed data selection methods make the case for search-based approaches considering their higher effectiveness and performance. We identified potential impacting factors on the effectiveness through the exploratory study and proposed methods to create better CPDP models.Conclusions: The proposal of numerous approaches in the literature over the last decade has led to progress in the area and the acquired knowledge and tools apply to many similar domains and can act as parts of academic curricula as well. Future directions of study can include searching for better validation data, better feature selection techniques, tuning the parameters of the search-based models, tuning hyper-parameters of learners, investigating the effects of multiple sources of optimization (learner, instances and features) and the impact of the class imbalance problem.Original papersOriginal papers are not included in the electronic version of the dissertation.Hosseini, S., Turhan, B., & Gunarathna, D. (2017). A systematic literature review and meta-analysis on cross project defect prediction. IEEE Transactions on Software Engineering, 45(2), 111-147. https://doi.org/10.1109/TSE.2017.2770124Self-archived versionHosseini, S., Turhan, B., & Mäntylä, M. (2016). Search based training data selection for cross project defect prediction. Proceedings of The 12th International Conference on Predictive Models and Data Analytics in Software Engineering. ACM. https://doi.org/10.1145/2972958.2972964Hosseini, S., Turhan, B., & Mäntylä, M. (2018). A benchmark study on the effectiveness of search-based data selection and feature selection for cross project defect prediction. Information and Software Technology, 95, 296-312. https://doi.org/10.1016/j.infsof.2017.06.004Self-archived versionHosseini, S., & Turhan, B. (2018). An exploratory study of search based training data selection for cross project defect prediction." 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE. https://doi.org/10.1109/SEAA.2018.00048Self-archived versionHosseini, S., & Turhan, B. (2019). Iterative versus exhaustive data selection for cross project defect prediction: an extended replication study. Manuscript submitted for publication.TiivistelmäTausta: Tämä tutkimus edistää projektienvälisten virheiden ennustamisen nykytilan ymmärtämistä (CPDP) tutkimalla aihetta teemoissa, keskittyen erityisesti tiedollisiin lähestymistapoihin ja hakuperusteisen harjoitusdatan valintaan esittelemällä datan valintamenetelmiä ja tutkimalla niiden vaikutuksia. Tämän työn empiirinen todistusaineisto on koottu muodollisella systemaattisella kirjallisuuskatsauksella ja avoimen lähdekoodin projekteissa tehdyillä kokeilla.Tavoite: Pyrimme ymmärtämään ja tiivistämään tavan, jolla erilaisia datan käsittelyn lähestymistapoja käytetään CPDP:ssa sekä niiden potentiaalisia vaikutuksia suorituskykyyn. Lisäksi, tavoitteenamme on hyödyntää hakuperusteisia menetelmiä muodostamaan kehittyviä harjoitusdata-settejä suodattamaan epäolennaisia esiintymiä muista projekteista ennen koulutusta.Menetelmä: CPDP:n nykytilan kirjallisuuskatsauksen jälkeen tehtyjen tutkimusten avulla ehdotamme hakuperusteista menetelmää, jota kutsutaan geneettisen esiintymän valinnaksi (GIS). Todistamme alustavat havaintomme suorittamalla seuraavan tutkimuksen suurella joukolla datasettejä, joilla on useita ominaisuuksia. Jalostamme suunnittelupäätöksiämme käyttäen tutkivaa tutkimusta. Lopuksi, tutkimme vallitsevaa meta-oppimisen lähestymistapaa ja tarjoamme näkemyksiä sen suunnitteluun ja ehdotamme vaihtoehtoista, toistuvaa datan valintamenetelmää.Tulokset: Kirjallisuuskatsaus paljastaa CPDP:n heikomman suorituskyvyn verrattuna projektinsisäisten virheiden ennustamisen (WPDP) malleihin ja tarjoaa joukon primaaritutkimuksia, joita voidaan käyttää perustana myöhemmälle tutkimukselle. Ehdottamamme datan valintamenetelmät puoltavat hakuperusteisten menetelmiä niiden paremman tehokkuuden ja suorituskyvyn vuoksi. Tunnistimme potentiaalisia tehokuuteen vaikuttavia tekijöitä tutkivien tutkimusten avulla ja ehdotimme metodeja parempien CPDP mallien luomiseksi.Johtopäätökset: Viime vuosikymmenten aikana kirjallisuudessa esitellyt lukuisat menetelmät ovat edistäneet alaa ja hankittu tieto ja työkalut soveltuvat monille samanlaisille alueille ja voivat toimia myös osana akateemisia opetussuunnitelmia. Tutkimuksen tulevat linjaukset voivat sisältää validointiin paremmin soveltuvan datan haun, paremmat ominaisuuksien valintatekniikat, hakuperusteisten mallien parametrien hienosäädön, oppijoiden hyper-parametrien hienosäädön, tutkimuksen useiden optimoinnin lähteiden vaikutuksista (oppija, esiintymät, ominaisuudet) ja luokan epätasapaino-ongelman vaikutuksesta.OsajulkaisutOsajulkaisut eivät sisälly väitöskirjan elektroniseen versioon.Hosseini, S., Turhan, B., & Gunarathna, D. (2017). A systematic literature review and meta-analysis on cross project defect prediction. IEEE Transactions on Software Engineering, 45(2), 111-147. https://doi.org/10.1109/TSE.2017.2770124Rinnakkaistallennettu versioHosseini, S., Turhan, B., & Mäntylä, M. (2016). Search based training data selection for cross project defect prediction. Proceedings of The 12th International Conference on Predictive Models and Data Analytics in Software Engineering. ACM. https://doi.org/10.1145/2972958.2972964Hosseini, S., Turhan, B., & Mäntylä, M. (2018). A benchmark study on the effectiveness of search-based data selection and feature selection for cross project defect prediction. Information and Software Technology, 95, 296-312. https://doi.org/10.1016/j.infsof.2017.06.004Rinnakkaistallennettu versioHosseini, S., & Turhan, B. (2018). An exploratory study of search based training data selection for cross project defect prediction." 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE. https://doi.org/10.1109/SEAA.2018.00048Rinnakkaistallennettu versioHosseini, S., & Turhan, B. (2019). Iterative versus exhaustive data selection for cross project defect prediction: an extended replication study. Manuscript submitted for publication.Academic dissertation to be presented, with the assent of the Doctoral Training Committee of Information Technology and Electrical Engineering of the University of Oulu, for public defence in the Oulun Puhelin auditorium (L5), Linnanmaa, on 5 December 2019, at 12 noonAbstract
Context: This study contributes to the understanding of the current state of cross-project defect prediction (CPDP) by investigating the topic in themes, with special focus on data approaches and covering search-based training data selection, by proposing data selection methods and investigating their impact. The empirical evidence for this work is collected through a formal systematic literature review method for the review, and from experiments on open source projects.
Objective: We aim to understand and summarize the manner in which various data manipulation approaches are used in CPDP and their potential impacts on performance. Further, we aim at utilizing search-based methods to produce evolving training data sets to filter irrelevant instances from other projects before training.
Method: Through a series of studies following the literature review of current state of CPDP, we propose a search-based method called genetic instance selection (GIS). We validate our initial findings by conducting the next study on a large set of data sets with multiple feature sets. We refine our design decisions using an exploratory study. Finally, we investigate an existing meta-learning approach, provide insights on its design and propose an alternative iterative data selection method.
Results: The literature review reveals lower performances of CPDP in comparison with within project defect prediction (WPDP) models and provides a set of primary studies to be used as the basis for future research. Our proposed data selection methods make the case for search-based approaches considering their higher effectiveness and performance. We identified potential impacting factors on the effectiveness through the exploratory study and proposed methods to create better CPDP models.
Conclusions: The proposal of numerous approaches in the literature over the last decade has led to progress in the area and the acquired knowledge and tools apply to many similar domains and can act as parts of academic curricula as well. Future directions of study can include searching for better validation data, better feature selection techniques, tuning the parameters of the search-based models, tuning hyper-parameters of learners, investigating the effects of multiple sources of optimization (learner, instances and features) and the impact of the class imbalance problem.Tiivistelmä
Tausta: Tämä tutkimus edistää projektienvälisten virheiden ennustamisen nykytilan ymmärtämistä (CPDP) tutkimalla aihetta teemoissa, keskittyen erityisesti tiedollisiin lähestymistapoihin ja hakuperusteisen harjoitusdatan valintaan esittelemällä datan valintamenetelmiä ja tutkimalla niiden vaikutuksia. Tämän työn empiirinen todistusaineisto on koottu muodollisella systemaattisella kirjallisuuskatsauksella ja avoimen lähdekoodin projekteissa tehdyillä kokeilla.
Tavoite: Pyrimme ymmärtämään ja tiivistämään tavan, jolla erilaisia datan käsittelyn lähestymistapoja käytetään CPDP:ssa sekä niiden potentiaalisia vaikutuksia suorituskykyyn. Lisäksi, tavoitteenamme on hyödyntää hakuperusteisia menetelmiä muodostamaan kehittyviä harjoitusdata-settejä suodattamaan epäolennaisia esiintymiä muista projekteista ennen koulutusta.
Menetelmä: CPDP:n nykytilan kirjallisuuskatsauksen jälkeen tehtyjen tutkimusten avulla ehdotamme hakuperusteista menetelmää, jota kutsutaan geneettisen esiintymän valinnaksi (GIS). Todistamme alustavat havaintomme suorittamalla seuraavan tutkimuksen suurella joukolla datasettejä, joilla on useita ominaisuuksia. Jalostamme suunnittelupäätöksiämme käyttäen tutkivaa tutkimusta. Lopuksi, tutkimme vallitsevaa meta-oppimisen lähestymistapaa ja tarjoamme näkemyksiä sen suunnitteluun ja ehdotamme vaihtoehtoista, toistuvaa datan valintamenetelmää.
Tulokset: Kirjallisuuskatsaus paljastaa CPDP:n heikomman suorituskyvyn verrattuna projektinsisäisten virheiden ennustamisen (WPDP) malleihin ja tarjoaa joukon primaaritutkimuksia, joita voidaan käyttää perustana myöhemmälle tutkimukselle. Ehdottamamme datan valintamenetelmät puoltavat hakuperusteisten menetelmiä niiden paremman tehokkuuden ja suorituskyvyn vuoksi. Tunnistimme potentiaalisia tehokuuteen vaikuttavia tekijöitä tutkivien tutkimusten avulla ja ehdotimme metodeja parempien CPDP mallien luomiseksi.
Johtopäätökset: Viime vuosikymmenten aikana kirjallisuudessa esitellyt lukuisat menetelmät ovat edistäneet alaa ja hankittu tieto ja työkalut soveltuvat monille samanlaisille alueille ja voivat toimia myös osana akateemisia opetussuunnitelmia. Tutkimuksen tulevat linjaukset voivat sisältää validointiin paremmin soveltuvan datan haun, paremmat ominaisuuksien valintatekniikat, hakuperusteisten mallien parametrien hienosäädön, oppijoiden hyper-parametrien hienosäädön, tutkimuksen useiden optimoinnin lähteiden vaikutuksista (oppija, esiintymät, ominaisuudet) ja luokan epätasapaino-ongelman vaikutuksesta
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Results from an ethnographically-informed study in the context of test driven development
No full textBackground: Test-driven development (TDD) is an iterative software development technique where unit tests are defined before production code. Previous studies fail to analyze the values, beliefs, and assumptions that inform and shape TDD. Aim: We designed and conducted a qualitative study to understand the values, beliefs, and assumptions of TDD. In particular, we sought to understand how novice and professional software developers, arranged in pairs (a driver and a pointer), perceive and apply TDD. Method: 14 novice software developers, i.e., graduate students in Computer Science at the University of Basilicata, and six professional software developers (with one to 10 years work experience) participated in our ethnographicallyinformed study. We asked the participants to implement a new feature for an existing software written in Java. We immersed ourselves in the context of the study, and collected data by means of contemporaneous field notes, audio recordings, and other artifacts. Results: A number of insights emerge from our analysis of the collected data, the main ones being: (i) refactoring (one of the phases of TDD) is not performed as often as the process requires and it is considered less important than other phases, (ii) the most important phase is implementation, (iii) unit tests are almost never up-to-date, (iv) participants first build a sort of mental model of the source code to be implemented and only then write test cases on the basis of this model; and (v) apart from minor differences, professional developers and students applied TDD in a similar fashion. Conclusions: Developers write quick-and-dirty production code to pass the tests and ignore refactoring.Copyright is held by the owner/auther(s)
Poster: The effect of noise on requirements comprehension
No full textWe conducted a controlled experiment with 55 final-year undergraduate students in Computer Science. We asked them to comprehend functional requirements exposing them or not to noise. We did not observe any effect of noise on requirements comprehension
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Topiramate and accommodation: Does topiramate cause accommodative dysfunction?
No full textObjective: To investigate the accommodation function in topiramate users. Design:Case-control clinical study. Participants:The participants included 16 controls and 22 patients using 100 mg/kg topiramate who were diagnosed with migraine according to the International Classification of Headache Disorders, second edition criteria. Methods:One-minute dynamic measurements of refraction with accommodation stimuli of 0 D, 2 D, 2.5 D, 3 D, 4 D, and 5 D were obtained using the open field refractometer WAM-5500 in. Results:In most of the accommodation stimuli ranges (0 D, 2.5 D, 3 D, and 5 D), topiramate users had a significantly higher accommodative lag compared with controls (p=0.028, p =0.014, p=0.011, and p=0.011, respectively). The most important causes of accommodative lag were found to be accommodation stimulus and inclusion in the topiramate group (p<0.001, R-2=0.32, 95% CI 0.22-0.37 and 0.42-0.91, respectively). Multivariate linear regression analysis revealed that the 2 most important predictors of accommodative lag were accommodation stimulus and age (p<0.001, r=0.51, 95% CI 0.31-0.32 and 0.67-0.69, respectively) CONCLUSIONS: Even after adjustment for age, accommodative lag is greater across several accommodative stimulus levels in patients using topiramate, which may be related to visual symptoms in topiramate users
Students' and professionals' perceptions of test-driven development: A focus group study
No full textWe have conducted a qualitative investigation on test-driven development (TDD) with focus groups to develop insights on the opinions of developers using TDD regarding the unintuitive process involved, its claimed effects, as well as the context factors that can facilitate (or hinder) its application. In particular, we conducted two focus group sessions: one with professionals and another with Master students in Computer Science. We used thematic analysis template (TAT) method for identifying patterns, themes, and interpretations in gathered data. We obtained a number of results that can be summarized as follows: (i) applying TDD without knowing advanced unit testing techniques can be difficult; (ii) refactoring (one of the phases of TDD) is not done as often as the process requires; (iii) there is a need for live feedback to let developers understand if TDD is being applied correctly; and (iv) the usefulness of TDD hinges on task and domain to which it is applied to
Four commentaries on the use of students and professionals in empirical software engineering experiments
No full textThe relative pros and cons of using students or practitioners in experiments in empirical software engineering have been discussed for a long time and continue to be an important topic. Following the recent publication of “Empirical software engineering experts on the use of students and professionals in experiments” by Falessi, Juristo, Wohlin, Turhan, Münch, Jedlitschka, and Oivo (EMSE, February 2018) we received a commentary by Sjøberg and Bergersen. Given that the topic is of great methodological interest to the community and requires nuanced treatment, we invited two editorial board members, Martin Shepperd and Per Runeson, respectively, to provide additional views
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
The effect of noise on software engineers' performance
No full textBackground: Noise, defined as an unwanted sound, is one of the commonest factors that could affect people's performance in their daily work activities. The software engineering research community has marginally investigated the effects of noise on software engineers' performance. Aims: We studied if noise affects software engineers' performance in: (i) comprehending functional requirements and (ii) fixing faults in source code. Method: We conducted two experiments with final-year undergraduate students in Computer Science. In the first experiment, we asked 55 students to comprehend functional requirements exposing them or not to noise, while in the second experiment 42 students were asked to fix faults in Java code. Results: The participants in the second experiment, when exposed to noise, had significantly worse performance in fixing faults in source code. On the other hand, we did not observe any statistically significant difference in the first experiment. Conclusions: Fixing faults in source code seems to be more vulnerable to noise than comprehending functional requirements
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