6 results on '"Penta, Massimiliano Di"'
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2. License usage and changes: a large-scale study on gitHub
- Author
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Vendome, Christopher, Bavota, Gabriele, Penta, Massimiliano Di, Linares-Vásquez, Mario, German, Daniel, and Poshyvanyk, Denys
- Published
- 2017
- Full Text
- View/download PDF
3. How Is Video Game Development Different from Software Development in Open Source?
- Author
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Pascarella, Luca, Palomba, Fabio, Penta, Massimiliano Di, Bacchelli, Alberto, and University of Zurich
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Video game development ,10009 Department of Informatics ,business.industry ,Computer science ,Suite ,05 social sciences ,Software development ,ComputingMilieux_PERSONALCOMPUTING ,020207 software engineering ,Context (language use) ,video games ,02 engineering and technology ,000 Computer science, knowledge & systems ,1712 Software ,Empirical research ,Software ,Empirical studies ,0502 economics and business ,0202 electrical engineering, electronic engineering, information engineering ,Software system ,Game Developer ,business ,Software engineering ,mining software repository ,050203 business & management - Abstract
Recent research has provided evidence that, in the industrial context, developing video games diverges from developing software systems in other domains, such as office suites and system utilities. In this paper, we consider video game development in the open source system (OSS) context. Specifically, we investigate how developers contribute to video games vs. non-games by working on different kinds of artifacts, how they handle malfunctions, and how they perceive the development process of their projects. To this purpose, we conducted a mixed, qualitative and quantitative study on a broad suite of 60 OSS projects. Our results confirm the existence of significant differences between game and non-game development, in terms of how project resources are organized and in the diversity of developers’ specializations. Moreover, game developers respond- ing to our survey perceive more difficulties than other developers when reusing code as well as performing automated testing, and they lack a clear overview of their system’s requirements.
- Published
- 2018
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4. Mining Version Histories for Detecting Code Smells.
- Author
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Palomba, Fabio, Bavota, Gabriele, Penta, Massimiliano Di, Oliveto, Rocco, Poshyvanyk, Denys, and De Lucia, Andrea
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MINERAL industries ,STRUCTURAL analysis (Engineering) ,STRUCTURAL engineering software ,CIPHERS ,FAULT-tolerant computing ,STRUCTURAL reliability ,COMPUTER software - Abstract
Code smells are symptoms of poor design and implementation choices that may hinder code comprehension, and possibly increase change- and fault-proneness. While most of the detection techniques just rely on structural information, many code smells are intrinsically characterized by how code elements change over time. In this paper, we propose H istorical Information for Smell deTection (HIST), an approach exploiting change history information to detect instances of five different code smells, namely Divergent Change, Shotgun Surgery, Parallel Inheritance, Blob, and Feature Envy. We evaluate HIST in two empirical studies. The first, conducted on 20 open source projects, aimed at assessing the accuracy of HIST in detecting instances of the code smells mentioned above. The results indicate that the precision of HIST ranges between 72 and 86 percent, and its recall ranges between 58 and 100 percent. Also, results of the first study indicate that HIST is able to identify code smells that cannot be identified by competitive approaches solely based on code analysis of a single system’s snapshot. Then, we conducted a second study aimed at investigating to what extent the code smells detected by HIST (and by competitive code analysis techniques) reflect developers’ perception of poor design and implementation choices. We involved 12 developers of four open source projects that recognized more than 75 percent of the code smell instances identified by HIST as actual design/implementation problems. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
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5. The Impact of API Change- and Fault-Proneness on the User Ratings of Android Apps.
- Author
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Bavota, Gabriele, Linares-Vasquez, Mario, Bernal-Cardenas, Carlos Eduardo, Penta, Massimiliano Di, Oliveto, Rocco, and Poshyvanyk, Denys
- Subjects
INFORMATION technology ,MARKET share ,DISEASE susceptibility ,COMPUTER software development - Abstract
The mobile apps market is one of the fastest growing areas in the information technology. In digging their market share, developers must pay attention to building robust and reliable apps. In fact, users easily get frustrated by repeated failures, crashes, and other bugs; hence, they abandon some apps in favor of their competition. In this paper we investigate how the fault- and change-proneness of APIs used by Android apps relates to their success estimated as the average rating provided by the users to those apps. First, in a study conducted on 5,848 (free) apps, we analyzed how the ratings that an app had received correlated with the fault- and change-proneness of the APIs such app relied upon. After that, we surveyed 45 professional Android developers to assess (i) to what extent developers experienced problems when using APIs, and (ii) how much they felt these problems could be the cause for unfavorable user ratings. The results of our studies indicate that apps having high user ratings use APIs that are less fault- and change-prone than the APIs used by low rated apps. Also, most of the interviewed Android developers observed, in their development experience, a direct relationship between problems experienced with the adopted APIs and the users’ ratings that their apps received. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
6. Improving Multi-Objective Test Case Selection by Injecting Diversity in Genetic Algorithms.
- Author
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Panichella, Annibale, Oliveto, Rocco, Penta, Massimiliano Di, and De Lucia, Andrea
- Subjects
GENETIC algorithms ,COMBINATORIAL optimization ,SINGULAR value decomposition ,MATRICES (Mathematics) ,PARTICLE swarm optimization - Abstract
A way to reduce the cost of regression testing consists of selecting or prioritizing subsets of test cases from a test suite according to some criteria. Besides greedy algorithms, cost cognizant additional greedy algorithms, multi-objective optimization algorithms, and multi-objective genetic algorithms (MOGAs), have also been proposed to tackle this problem. However, previous studies have shown that there is no clear winner between greedy and MOGAs, and that their combination does not necessarily produce better results. In this paper we show that the optimality of MOGAs can be significantly improved by diversifying the solutions (sub-sets of the test suite) generated during the search process. Specifically, we introduce a new MOGA, coined as DIversity based Genetic Algorithm (DIV-GA), based on the mechanisms of orthogonal design and orthogonal evolution that increase diversity by injecting new orthogonal individuals during the search process. Results of an empirical study conducted on eleven programs show that DIV-GA outperforms both greedy algorithms and the traditional MOGAs from the optimality point of view. Moreover, the solutions (sub-sets of the test suite) provided by DIV-GA are able to detect more faults than the other algorithms, while keeping the same test execution cost. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
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