Kuvausmenetelmänvalinta ja käyttöönotto ohjelmistotuoteorganisaatiossa

Useiden pitkän kehityskaaren ohjelmistojen ylläpitäminen ja kehittäminen on vaikeaa, sillä niiden dokumentaatio on vajaata tai vanhentunutta. Tässä diplomityössä etsitään ratkaisua tällaisen ohjelmiston ja sen taustalla olevan järjestelmän kuvaukseen. Tavoitteina on tukea nykyisen ohjelmiston ylläpitoa ja uuden työvoiman perehdyttämistä. Tavoitteena on myös pohjustaa uuden korvaavan ohjelmiston suunnittelua kuvaamalla nykyiseen järjestelmään sitoutunutta sovellusalueosaamista. Työssä kehitetään kuvausmenetelmä järjestelmän kuvaamiseen hierarkkisesti laitteistotason yleiskuvauksesta ohjelmiston luokkarakenteeseen sekä toiminnallisuuteen asti. Laite- ja luokkarakennekuvaukset ovat rakenteellisia kuvauksia, joiden tehtävänä on selittää järjestelmän ja sen osien kokoonpano. Toiminnallisuudesta kertovat kuvaukset on toteutettu käyttötapauskuvauksina. Työssä keskityttiin erityisesti kohdejärjestelmän keskeisen ohjelmiston ja tietokannan kuvaamiseen. Ohjelmistosta valittiin tärkeimmät ja eniten sovellusalueen tietotaitoa sisältävät osat, joista työssä luotiin esimerkkikuvaukset. Kuvauksia on kehitettyä menetelmää hyödyntäen helppo laajentaa tarpeiden mukaan paitsi ohjelmiston muihin osiin, myös laitteiston ja järjestelmän kuvaamiseen kokonaisuudessaan syvemmin.

Tietämyksen välittäminen ohjelmistotestausorganisaatioissa

Ohjelmistotestaus on suuri ja kasvava kustannuserä ohjelmistotuotannossa. Kirjallisuudessa mainittu ohjelmistotestauksen kustannusten osuus on noin 50% ohjelmistokehityshankkeiden budjetista. Tutkimusprojektin tarkoituksena on selvittää, kuinka ohjelmistotestauksen kustannusten kasvu saataisiin pysähtymään tai laskemaan laatua menettämättä. Tässä työssä keskitytään laadullisen analyysin kautta selvittämään ja ymmärtämään tietämyksen välittämistä ohjelmistotestausorganisaatiossa. Tutkimusmateriaali on kerätty haastattelemalla26 organisaatioyksikön edustajia. Näiden organisaatioyksiköiden joukosta on edelleen valittu viisi organisaatioyksikköä lähempään tarkasteluun. Työssä havaittiin muun muassa, että tuotesuuntautuneessa ohjelmistokehityksessä tietämystä on vaivattomampi kodifioida. Esimerkiksi testitapausten määrittäminen ennakkoon on tällöin helpompaa. Kodifiointi mahdollistaa myös testauksen laajamittaisen ulkoistamisen, sillä kodifioitua tietämystä on helpompi välittää. Räätälöityjen järjestelmien testaamisessa tarvittava tietämys vaikuttaa olevan suurelta osin hiljaista, esimerkiksi sovellusalueosaaminen painottuu enemmän.

Thinking Outside the Box: Enhancing Science Teaching by Combining (Instead of Contrasting) Laboratory and Simulation Activities

The focus of the present work was on 10- to 12-year-old elementary school students’ conceptual learning outcomes in science in two specific inquiry-learning environments, laboratory and simulation. The main aim was to examine if it would be more beneficial to combine than contrast simulation and laboratory activities in science teaching. It was argued that the status quo where laboratories and simulations are seen as alternative or competing methods in science teaching is hardly an optimal solution to promote students’ learning and understanding in various science domains. It was hypothesized that it would make more sense and be more productive to combine laboratories and simulations. Several explanations and examples were provided to back up the hypothesis. In order to test whether learning with the combination of laboratory and simulation activities can result in better conceptual understanding in science than learning with laboratory or simulation activities alone, two experiments were conducted in the domain of electricity. In these experiments students constructed and studied electrical circuits in three different learning environments: laboratory (real circuits), simulation (virtual circuits), and simulation-laboratory combination (real and virtual circuits were used simultaneously). In order to measure and compare how these environments affected students’ conceptual understanding of circuits, a subject knowledge assessment questionnaire was administered before and after the experimentation. The results of the experiments were presented in four empirical studies. Three of the studies focused on learning outcomes between the conditions and one on learning processes. Study I analyzed learning outcomes from experiment I. The aim of the study was to investigate if it would be more beneficial to combine simulation and laboratory activities than to use them separately in teaching the concepts of simple electricity. Matched-trios were created based on the pre-test results of 66 elementary school students and divided randomly into a laboratory (real circuits), simulation (virtual circuits) and simulation-laboratory combination (real and virtual circuits simultaneously) conditions. In each condition students had 90 minutes to construct and study various circuits. The results showed that studying electrical circuits in the simulation–laboratory combination environment improved students’ conceptual understanding more than studying circuits in simulation and laboratory environments alone. Although there were no statistical differences between simulation and laboratory environments, the learning effect was more pronounced in the simulation condition where the students made clear progress during the intervention, whereas in the laboratory condition students’ conceptual understanding remained at an elementary level after the intervention. Study II analyzed learning outcomes from experiment II. The aim of the study was to investigate if and how learning outcomes in simulation and simulation-laboratory combination environments are mediated by implicit (only procedural guidance) and explicit (more structure and guidance for the discovery process) instruction in the context of simple DC circuits. Matched-quartets were created based on the pre-test results of 50 elementary school students and divided randomly into a simulation implicit (SI), simulation explicit (SE), combination implicit (CI) and combination explicit (CE) conditions. The results showed that when the students were working with the simulation alone, they were able to gain significantly greater amount of subject knowledge when they received metacognitive support (explicit instruction; SE) for the discovery process than when they received only procedural guidance (implicit instruction: SI). However, this additional scaffolding was not enough to reach the level of the students in the combination environment (CI and CE). A surprising finding in Study II was that instructional support had a different effect in the combination environment than in the simulation environment. In the combination environment explicit instruction (CE) did not seem to elicit much additional gain for students’ understanding of electric circuits compared to implicit instruction (CI). Instead, explicit instruction slowed down the inquiry process substantially in the combination environment. Study III analyzed from video data learning processes of those 50 students that participated in experiment II (cf. Study II above). The focus was on three specific learning processes: cognitive conflicts, self-explanations, and analogical encodings. The aim of the study was to find out possible explanations for the success of the combination condition in Experiments I and II. The video data provided clear evidence about the benefits of studying with the real and virtual circuits simultaneously (the combination conditions). Mostly the representations complemented each other, that is, one representation helped students to interpret and understand the outcomes they received from the other representation. However, there were also instances in which analogical encoding took place, that is, situations in which the slightly discrepant results between the representations ‘forced’ students to focus on those features that could be generalised across the two representations. No statistical differences were found in the amount of experienced cognitive conflicts and self-explanations between simulation and combination conditions, though in self-explanations there was a nascent trend in favour of the combination. There was also a clear tendency suggesting that explicit guidance increased the amount of self-explanations. Overall, the amount of cognitive conflicts and self-explanations was very low. The aim of the Study IV was twofold: the main aim was to provide an aggregated overview of the learning outcomes of experiments I and II; the secondary aim was to explore the relationship between the learning environments and students’ prior domain knowledge (low and high) in the experiments. Aggregated results of experiments I & II showed that on average, 91% of the students in the combination environment scored above the average of the laboratory environment, and 76% of them scored also above the average of the simulation environment. Seventy percent of the students in the simulation environment scored above the average of the laboratory environment. The results further showed that overall students seemed to benefit from combining simulations and laboratories regardless of their level of prior knowledge, that is, students with either low or high prior knowledge who studied circuits in the combination environment outperformed their counterparts who studied in the laboratory or simulation environment alone. The effect seemed to be slightly bigger among the students with low prior knowledge. However, more detailed inspection of the results showed that there were considerable differences between the experiments regarding how students with low and high prior knowledge benefitted from the combination: in Experiment I, especially students with low prior knowledge benefitted from the combination as compared to those students that used only the simulation, whereas in Experiment II, only students with high prior knowledge seemed to benefit from the combination relative to the simulation group. Regarding the differences between simulation and laboratory groups, the benefits of using a simulation seemed to be slightly higher among students with high prior knowledge. The results of the four empirical studies support the hypothesis concerning the benefits of using simulation along with laboratory activities to promote students’ conceptual understanding of electricity. It can be concluded that when teaching students about electricity, the students can gain better understanding when they have an opportunity to use the simulation and the real circuits in parallel than if they have only the real circuits or only a computer simulation available, even when the use of the simulation is supported with the explicit instruction. The outcomes of the empirical studies can be considered as the first unambiguous evidence on the (additional) benefits of combining laboratory and simulation activities in science education as compared to learning with laboratories and simulations alone.

Applications of graph transformation in tools for domain-specific modeling languages

The use of domain-specific languages (DSLs) has been proposed as an approach to cost-e ectively develop families of software systems in a restricted application domain. Domain-specific languages in combination with the accumulated knowledge and experience of previous implementations, can in turn be used to generate new applications with unique sets of requirements. For this reason, DSLs are considered to be an important approach for software reuse. However, the toolset supporting a particular domain-specific language is also domain-specific and is per definition not reusable. Therefore, creating and maintaining a DSL requires additional resources that could be even larger than the savings associated with using them. As a solution, di erent tool frameworks have been proposed to simplify and reduce the cost of developments of DSLs. Developers of tool support for DSLs need to instantiate, customize or configure the framework for a particular DSL. There are di erent approaches for this. An approach is to use an application programming interface (API) and to extend the basic framework using an imperative programming language. An example of a tools which is based on this approach is Eclipse GEF. Another approach is to configure the framework using declarative languages that are independent of the underlying framework implementation. We believe this second approach can bring important benefits as this brings focus to specifying what should the tool be like instead of writing a program specifying how the tool achieves this functionality. In this thesis we explore this second approach. We use graph transformation as the basic approach to customize a domain-specific modeling (DSM) tool framework. The contributions of this thesis includes a comparison of di erent approaches for defining, representing and interchanging software modeling languages and models and a tool architecture for an open domain-specific modeling framework that e ciently integrates several model transformation components and visual editors. We also present several specific algorithms and tool components for DSM framework. These include an approach for graph query based on region operators and the star operator and an approach for reconciling models and diagrams after executing model transformation programs. We exemplify our approach with two case studies MICAS and EFCO. In these studies we show how our experimental modeling tool framework has been used to define tool environments for domain-specific languages.

The northern provinces of Sweden and Norway with part I, of Russia / published under the superintendence of the Society for the Diffusion of Useful Knowledge.

[N. 1:4400000].