@Article{eInformatica2016Art1, Title = {{ABC-CAG: Covering Array Generator for Pair-wise Testing Using Artificial Bee Colony Algorithm}}, Author = {Priti Bansal and Sangeeta Sabharwal and Nitish Mittal and Sarthak Arora}, Journal = {e-Informatica Software Engineering Journal}, Year = {2016}, Number = {1}, Pages = {9--29}, Volume = {10}, Abstract = {Testing is an indispensable part of the software development life cycle. It is performed to improve the performance, quality and reliability of the software. Various types of testing such as functional testing and structural testing are performed on software to uncover the faults caused by an incorrect code, interaction of input parameters, etc. One of the major factors in deciding the quality of testing is the design of relevant test cases which is crucial for the success of testing. In this paper we concentrate on generating test cases to uncover faults caused by the interaction of input parameters. It is advisable to perform thorough testing but the number of test cases grows exponentially with the increase in the number of input parameters, which makes exhaustive testing of interaction of input parameters imprudent. An alternative to exhaustive testing is combinatorial interaction testing (CIT) which requires that every t-way interaction of input parameters be covered by at least one test case. Here, we present a novel strategy ABC-CAG (Artificial Bee Colony-Covering Array Generator) based on the Artificial Bee Colony (ABC) algorithm to generate covering an array and a mixed covering array for pair-wise testing. The proposed ABC-CAG strategy is implemented in a tool and experiments are conducted on various benchmark problems to evaluate the efficacy of the proposed approach. Experimental results show that ABC-CAG generates better/comparable results as compared to the existing state-of-the-art algorithms.}, Doi = {10.5277/e-Inf160101}, Keywords = {combinatorial interaction testing, pair-wise testing, covering array, artificial bee colony}, } @Article{eInformatica2016Art2, Title = {{Reducing the Number of Higher-order Mutants with the Aid of Data Flow}}, Author = {Ahmed S. Ghiduk}, Journal = {e-Informatica Software Engineering Journal}, Year = {2016}, Number = {1}, Pages = {31--49}, Volume = {10}, Abstract = {Higher-order mutants are created by injecting two or more mutations into the original program, while first-order mutants are generated by seeding single faults in the original program. Mutant generation is anobreakspace {}key stage of mutation testing which is computationally very expensive, especially in the case of higher-order mutants. Although many mutation testing techniques have been developed to construct the first-order mutants, anobreakspace {}very small number of techniques have been presented to generate the higher-order mutants because of the exponential growth of the number of higher-order mutants, and the coupling effect between higher-order and first-order mutants. To overcome the exponential explosion in the number of higher-order mutants considered, this paper introduces anobreakspace {}new technique for generating anobreakspace {}reduced set of higher-order mutants. The proposed technique utilizes anobreakspace {}data-flow analysis to decrease the number of mutation points through the program under test and consequently reduce the number of higher-order mutants. In this technique only positions of emph {defs} and emph {uses} are considered as locations to seed the mutation. The generated set of higher-order mutants consists of anobreakspace {}reduced number of mutants, which reduces the costs of higher-order mutation testing. In addition, the proposed technique can generate the higher-order mutants directly without generating the first-order mutants or by combining two or more first-order mutants. A set of experiments are conducted to evaluate the effectiveness of the proposed technique. The results of the conducted experiments are presented and compared with the results of the related work. These results showed that the proposed technique is more effective than the earlier techniques in generating higher-order mutants without affecting the efficiency of mutation testing.}, Doi = {10.5277/e-Inf160102}, Keywords = {mutation testing, first-order mutants, higher-order mutants, data-flow analysis}, } @Article{eInformatica2016Art3, Title = {{Automatic SUMO to UML translation}}, Author = {Bogumiła Hnatkowska}, Journal = {e-Informatica Software Engineering Journal}, Year = {2016}, Number = {1}, Pages = {51--67}, Volume = {10}, Abstract = {Existing ontologies are a~valuable source of domain knowledge. That knowledge could be extracted and reused to create domain models. The extraction process can be aided by tools that enable browsing ontology, marking interesting notions and automatic conversion of selected elements to other notations. The paper presents a~tool that can be used for SUMO to UML translation. Such transformation is feasible and results in a~high-quality domain model, which is consistent, correct, and complete providing that input ontology has the same features.}, Doi = {10.5277/e-Inf160103}, Keywords = {SUMO ontology, information retrieving, domain model, UML, class diagram}, } @Article{eInformatica2016Art4, Title = {{Highly Automated Agile Testing Process: An Industrial Case Study}}, Author = {Jarosław Berłowski and Patryk Chruściel and Marcin Kasprzyk and Iwona Konaniec and Marian Jureczko}, Journal = {e-Informatica Software Engineering Journal}, Year = {2016}, Number = {1}, Pages = {69--87}, Volume = {10}, Abstract = {This paper presents a description of an agile testing process in a medium size software project that is developed using Scrum. The research methods used is the case study were as follows: surveys, quantifiable project data sources and qualitative project members opinions were used for data collection. Challenges related to the testing process regarding a complex project environment and unscheduled releases were identified. Based on the obtained results, we concluded that the described approach addresses well the aforementioned issues. Therefore, recommendations were made with regard to the employed principles of agility, specifically: continuous integration, responding to change, test automation and test driven development. Furthermore, an efficient testing environment that combines a number of test frameworks (e.g. JUnit, Selenium, Jersey Test) with custom-developed simulators is presented.}, Doi = {10.5277/e-Inf160104}, Keywords = {software engineering, testing process, agile software development, case study}, } @Article{eInformatica2016Art5, Title = {{Software Startups -- A Research Agenda}}, Author = {Michael Unterkalmsteiner and Pekka Abrahamsson and XiaoFeng Wang and Anh Nguyen-Duc and Syed Shah and Sohaib Shahid Bajwa and Guido H. Baltes and Kieran Conboy and Eoin Cullina and Denis Dennehy and Henry Edison and Carlos Fernandez-Sanchez and Juan Garbajosa and Tony Gorschek and Eriks Klotins and Laura Hokkanen and Fabio Kon and Ilaria Lunesu and Michele Marchesi and Lorraine Morgan and Markku Oivo and Christoph Selig and Pertti Seppänen and Roger Sweetman and Pasi Tyrväinen and Christina Ungerer and Agustin Yagüe}, Journal = {e-Informatica Software Engineering Journal}, Year = {2016}, Number = {1}, Pages = {89--124}, Volume = {10}, Abstract = {Software startup companies develop innovative, software-intensive products within limited time frames and with few resources, searching for sustainable and scalable business models. Software startups are quite distinct from traditional mature software companies, but also from micro-, small-, and medium-sized enterprises, introducing new challenges relevant for software engineering research. This paper's research agenda focuses on software engineering in startups, identifying, in particular, 70+ research questions in the areas of supporting startup engineering activities, startup evolution models and patterns, ecosystems and innovation hubs, human aspects in software startups, applying startup concepts in non-startup environments, and methodologies and theories for startup research. We connect and motivate this research agenda with past studies in software startup research, while pointing out possible future directions. While all authors of this research agenda have their main background in Software Engineering or Computer Science, their interest in software startups broadens the perspective to the challenges, but also to the opportunities that emerge from multi-disciplinary research. Our audience is therefore primarily software engineering researchers, even though we aim at stimulating collaborations and research that crosses disciplinary boundaries. We believe that with this research agenda we cover a wide spectrum of the software startup industry current needs. }, Doi = {10.5277/e-Inf160105}, Keywords = {software startup, research agenda, software-intensive systems}, } @Article{eInformatica2016Art5, Title = {{Software Startups -- A Research Agenda}}, Author = {Michael Unterkalmsteiner and Pekka Abrahamsson and XiaoFeng Wang and Anh Nguyen-Duc and Syed Shah and Sohaib Shahid Bajwa and Guido H. Baltes and Kieran Conboy and Eoin Cullina and Denis Dennehy and Henry Edison and Carlos Fernandez-Sanchez and Juan Garbajosa and Tony Gorschek and Eriks Klotins and Laura Hokkanen and Fabio Kon and Ilaria Lunesu and Michele Marchesi and Lorraine Morgan and Markku Oivo and Christoph Selig and Pertti Seppänen and Roger Sweetman and Pasi Tyrväinen and Christina Ungerer and Agustin Yagüe}, Journal = {e-Informatica Software Engineering Journal}, Year = {2016}, Number = {1}, Pages = {89--124}, Volume = {10}, Abstract = {Software startup companies develop innovative, software-intensive products within limited time frames and with few resources, searching for sustainable and scalable business models. Software startups are quite distinct from traditional mature software companies, but also from micro-, small-, and medium-sized enterprises, introducing new challenges relevant for software engineering research. This paper's research agenda focuses on software engineering in startups, identifying, in particular, 70+ research questions in the areas of supporting startup engineering activities, startup evolution models and patterns, ecosystems and innovation hubs, human aspects in software startups, applying startup concepts in non-startup environments, and methodologies and theories for startup research. We connect and motivate this research agenda with past studies in software startup research, while pointing out possible future directions. While all authors of this research agenda have their main background in Software Engineering or Computer Science, their interest in software startups broadens the perspective to the challenges, but also to the opportunities that emerge from multi-disciplinary research. Our audience is therefore primarily software engineering researchers, even though we aim at stimulating collaborations and research that crosses disciplinary boundaries. We believe that with this research agenda we cover a wide spectrum of the software startup industry current needs. }, Doi = {10.5277/e-Inf160105}, Keywords = {software startup, research agenda, software-intensive systems}, } @Article{eInformatica2016Editorial, Title = {{Editorial}}, Author = {Zbigniew Huzar and Lech Madeyski}, Journal = {e-Informatica Software Engineering Journal}, Year = {2016}, Number = {1}, Pages = {7--8}, Volume = {10} }