16,910 research outputs found

    Automated analysis of feature models: Quo vadis?

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    Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

    Evolution, testing and configuration of variability intensive systems

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    Tesis descargada desde ResearchGateOne of the key characteristics of software is its ability to be adapted and configured to different scenarios. Recently, software variability has been studied as a first-class concept in different domains ranging from software product lines to pervasive systems. Variability is the ability of a software product to vary depending on different circumstances. Variability intensive systems are those software products where variability management is a core engineering activity. The varying parts of those systems are commonly modeled by us- ing different variability model flavors, being feature modeling one of the most common ones. Feature models were first introduced by Kang et al. back in 1990 and are a compact representation of a set of configurations in a variability intensive system. The large number of configurations that a feature model can encode makes the manual analysis of feature models an error prone and costly task. Then, computer-aided mechanisms appeared as a solution to extract useful information from feature models. This process of extracting information from feature models is known as ¿Automated Analysis of Feature models¿ that has been one of the main areas of research in the last years where more than thirty analysis operations have been proposed.Premio Extraordinario de Doctorado U

    A research review of quality assessment for software

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    Measures were recommended to assess the quality of software submitted to the AdaNet program. The quality factors that are important to software reuse are explored and methods of evaluating those factors are discussed. Quality factors important to software reuse are: correctness, reliability, verifiability, understandability, modifiability, and certifiability. Certifiability is included because the documentation of many factors about a software component such as its efficiency, portability, and development history, constitute a class for factors important to some users, not important at all to other, and impossible for AdaNet to distinguish between a priori. The quality factors may be assessed in different ways. There are a few quantitative measures which have been shown to indicate software quality. However, it is believed that there exists many factors that indicate quality and have not been empirically validated due to their subjective nature. These subjective factors are characterized by the way in which they support the software engineering principles of abstraction, information hiding, modularity, localization, confirmability, uniformity, and completeness

    Data collection procedures for the Software Engineering Laboratory (SEL) database

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    This document is a guidebook to collecting software engineering data on software development and maintenance efforts, as practiced in the Software Engineering Laboratory (SEL). It supersedes the document entitled Data Collection Procedures for the Rehosted SEL Database, number SEL-87-008 in the SEL series, which was published in October 1987. It presents procedures to be followed on software development and maintenance projects in the Flight Dynamics Division (FDD) of Goddard Space Flight Center (GSFC) for collecting data in support of SEL software engineering research activities. These procedures include detailed instructions for the completion and submission of SEL data collection forms

    Javan yksikkö- ja integraatiotestaus: JVM:n käyttäytymisvetoiset testaustyökalut vastaan JUnit

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    This master’s thesis studied how do Behavior-Driven Development testing frameworks change the testing of Java-code compared to JUnit. The research was done as a case study. The case study was conducted in industry context at Vincit Plc, were two projects changed new unit and integration tests classes to use a new BDD-testing framework instead of JUnit. Before designing the study methods, related research and their findings were reviewed to guide the study to inspect problematic areas found in unit testing. Case study data collection methods included surveys, interviews and test code analysis. Case study provided promising results for problematic areas highlighted by earlier research. To summarize the developer practice changes, the collected data displayed an increase in unit test case granularity. Results also displayed unanimously that BDD-testing frameworks guide to write more self-documenting tests than JUnit. The structure of BDD tests highlighted better the different parts of the test. Study also revealed that the majority of participants had easier time understanding tests and removing repetition from test code. Developer perception changes in testing included the majority of study participants enjoying writing of tests more than with JUnit. The same majority also perceived that BDD-testing frameworks promote in writing higher quality test code than JUnit. Generally new test code was perceived more understandable and maintainable than tests with JUnit, although this was not unanimous. Learning curve to be effective varied between studied frameworks. Tool support of BDD-testing frameworks for testing Java Spring Framework were found ranging from adequate to good. In conclusion, this thesis results provide small scale evidence that BDD-testing frameworks could potentially ease the maintainability and readability of unit and integration tests while same time rising the enjoyment in testing.Tässä diplomityössä tutkittiin, kuinka käyttäytymisvetoiset testisovelluskehykset muuttavat Java-koodin testausta verrattuna JUnit:iin. Tutkimus suoritettiin tapaustutkimuksen menetelmin Vincit Oy:ssa. Tutkimukseen valittiin kaksi projektia, joissa uudet yksikkö- ja integraatiotestausluokat kirjoitettiin käyttäytymisvetoisilla testaussovelluskehyksillä JUnit:in sijaan. Työhön liittyvät aiempien tutkimusten havainnot ohjasivat työtä tarkastelemaan näissä löydettyjä ongelmallisia alueita. Tiedonkeruukeinoina käytettiin kyselyitä, haastatteluita sekä testikoodin analyysia. Työn tulokset osoittautuivat lupaaviksi ratkaisuksi aiemmin löydettyihin ongelmallisiin seikkoihin. Kokonaisuudessaan sovelluskehittäjien testauskäytännöissä löytyi useita muutoksia. Yksikkötestien rakenne ohjautui aiempaa hienojakoisemmaksi. Tulokset osoittivat myös yksimielisesti, että käyttäytymisvetoiset testaussovelluskehykset ohjaavat kirjoittamaan aiempaa paremmin itseänsä dokumentoivia testejä. Myös testin eri loogiset osat olivat uusien testien rakenteesta helpommin luettavissa. Suurimmalla osalla tutkimukseen osallistuneista testit olivat aiempaa helpompia ymmärtää sekä niistä oli helpompi poistaa toistoa. Suurin osa koki testien kirjoittamisen myös aiempaa nautittavampana. Valtaosa vastaajista koki uusien menetelmien ohjaavan kirjoittamaan laadukkaampaa testikoodia kuin aiemmin. Yleisesti ottaen uutta testikoodia pidettiin ymmärrettävämpänä ja ylläpidettävämpänä kuin JUnit testejä, tosin ei täysin yksimielisesti. Oppimiskäyrä uusien testauskehyksien parissa vaihteli tutkittujen kehysten välillä. Java Spring-sovelluskehyksen testaustuki vaihteli riittävästä tuesta hyvään tukeen. Kokonaisuudessaan työ tarjosi pienessä skaalassa näyttöä siitä, että käyttäytymisvetoiset testaussovelluskehykset voivat mahdollisesti helpottaa yksikkö- ja integraatiotestien ylläpidettävyyttä, luettavuutta sekä koettua nautintoa näiden parissa
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