1,721,127 research outputs found
Markku’s software, systems and services : professor Markku Oivo’s celebration book
Full text linkAbstract
This book celebrates the career of Professor Markku Oivo and the impact he has had on software engineering research, education, and the global community. Professor Oivo’s work in empirical research has made a significant difference in the industry worldwide. His research connects theory and industry application, making it practical and relevant. This book also acknowledges Professor Oivo’s dedication to education and mentorship, which has influenced the careers of many scholars and professionals in the software industry. The book features original research, surveys, and discussions on current topics in software engineering by colleagues, former students, and peers.Tiivistelmä
Tämä kirja juhlistaa professori Markku Oivon uraa ja hänen vaikutustaan ohjelmistotekniikan tutkimukseen, koulutukseen ja maailmanlaajuiseen yhteisöön. Professori Oivon työ empiirisen ohjelmistotutkimuksen parissa on vaikuttanut merkittävästi alaan maailmanlaajuisesti. Hänen tutkimuksensa on yhdistänyt teorian ja teollisuuden sovellukset käytännöllisellä ja merkityksellisellä tavalla. Tämä kirja on myös tunnustus professori Oivon omistautuminen koulutukselle ja mentoroinnille, jolla on ollut kauaskantoisia vaikutuksia monien tutkijoiden ja ohjelmistoalan ammattilaisten uraan. Kirjassa on koottu yhteen kollegoiden ja entisten opiskelijoiden alkuperäistutkimuksia, selvityksiä ja erinäisiä keskusteluja ohjelmistotekniikan ajankohtaisista aiheista.Abstract
This book celebrates the career of Professor Markku Oivo and the impact he has had on software engineering research, education, and the global community. Professor Oivo’s work in empirical research has made a significant difference in the industry worldwide. His research connects theory and industry application, making it practical and relevant. This book also acknowledges Professor Oivo’s dedication to education and mentorship, which has influenced the careers of many scholars and professionals in the software industry. The book features original research, surveys, and discussions on current topics in software engineering by colleagues, former students, and peers.Tiivistelmä
Tämä kirja juhlistaa professori Markku Oivon uraa ja hänen vaikutustaan ohjelmistotekniikan tutkimukseen, koulutukseen ja maailmanlaajuiseen yhteisöön. Professori Oivon työ empiirisen ohjelmistotutkimuksen parissa on vaikuttanut merkittävästi alaan maailmanlaajuisesti. Hänen tutkimuksensa on yhdistänyt teorian ja teollisuuden sovellukset käytännöllisellä ja merkityksellisellä tavalla. Tämä kirja on myös tunnustus professori Oivon omistautuminen koulutukselle ja mentoroinnille, jolla on ollut kauaskantoisia vaikutuksia monien tutkijoiden ja ohjelmistoalan ammattilaisten uraan. Kirjassa on koottu yhteen kollegoiden ja entisten opiskelijoiden alkuperäistutkimuksia, selvityksiä ja erinäisiä keskusteluja ohjelmistotekniikan ajankohtaisista aiheista
How to Build Repeatable Experiments
No full textAssociated research group: Critical Systems Research GroupThe mantra of the PROMISE series is "repeatable, improvable, maybe refutable" software engineering experiments. This community has successfully created a library of reusable software engineering data sets. The next challenge in the PROMISE community will be to not only share data, but to share experiments. Our experience with existing data mining environments is that these tools are not suitable for publishing or sharing repeatable experiments.
OURMINE is an environment for the development of data mining experiments. OURMINE offers a succinct notation for describing experiments. Adding new tools to OURMINE, in a variety of languages, is a rapid and simple process. This makes it a useful research tool. Complicated graphical interfaces have been eschewed for simple command-line prompts. This simplifies the learning curve for data mining novices. The simplicity also encourages large scale modification and experimentation with the code.
In this paper, we show the OURMINE code required to reproduce a recent experiment checking how defect predictors learned from one site apply to another. This is an important result for the PROMISE community since it shows that our shared repository is not just a useful academic resource. Rather, it is a valuable resource industry: companies that lack the local data required to build those predictors can use PROMISE data to build defect predictors.Gay, Gregory; Menzies, Tim; Cukic, Bojan; Turhan, Burak. (2008). How to Build Repeatable Experiments. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/217362
Implications of Ceiling Effects in Defect Predictors
No full textAssociated research group: Critical Systems Research GroupContext: There are many methods that input static code features and output a predictor for faulty code modules. These data mining methods have hit a "performance ceiling"; i.e., some inherent upper bound on the amount of information offered by, say, static code features when identifying modules which contain faults. Objective: We seek an explanation for this ceiling effect. Perhaps static code features have "limited information content"; i.e. their information can be quickly and completely discovered by even simple learners. Method:An initial literature review documents the ceiling effect in other work. Next, using three sub-sampling techniques (under-, over-, and micro-sampling), we look for the lower useful bound on the number of training instances. Results: Using micro-sampling, we find that as few as 50 instances yield as much information as larger training sets. Conclusions: We have found much evidence for the limited information hypothesis. Further progress in learning defect predictors may not come from better algorithms. Rather, we need to be improving the information content of the training data, perhaps with case-based reasoning methods.Menzies, Tim; Turhan, Burak; Gay, Gregory; Bener, Ayse; Cukic, Bojan; Jiang, Yue. (2008). Implications of Ceiling Effects in Defect Predictors. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/217361
Findings from a multi-method study on test-driven development
Full text linkContext Test-driven development (TDD) is an iterative software development practice where unit tests are defined before production code. A number of quantitative empirical investigations have been conducted about this practice. The results are contrasting and inconclusive. In addition, previous studies fail to analyze the values, beliefs, and assumptions that inform and shape TDD. Objective We present a study designed, and conducted to understand the values, beliefs, and assumptions about TDD. Participants were novice and professional software developers. Method We conducted an ethnographically-informed study with 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). The participants worked on the implementation of a new feature for an existing software written in Java. We immersed ourselves in the context of our study. We collected qualitative information by means of audio recordings, contemporaneous field notes, and other kinds of artifacts. We collected quantitative data from the integrated development environment to support or refute the ethnography results. Results The main insights of our study can be summarized as follows: (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, and (iv) participants first build in their mind a sort of model of the source code to be implemented and only then write test cases. The analysis of the quantitative data supported the following qualitative findings: (i), (iii), and (iv). Conclusions Developers write quick-and-dirty production code to pass the tests, do not update their tests often, and ignore refactoring
Students' and professionals' perceptions of test-driven development: A focus group study
Full text linkWe 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
Results from an ethnographically-informed study in the context of test driven development
Full text linkBackground: 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)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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