Current Issue : July - September Volume : 2015 Issue Number : 3 Articles : 5 Articles
Background: Frameworks are used to enhance the quality of applications and the\nproductivity of the development process, since applications may be designed and\nimplemented by reusing framework classes. However, frameworks are hard to develop,\nlearn and reuse, due to their adaptive nature. From Feature to Frameworks (F3) is an\napproach that supports framework development in two steps: Domain Modeling, to\nmodel domain features of the framework; and Framework Construction, to develop\nframework source-code based on the modeled domain and on patterns provided by\nthis approach.\nMethods: In this article, it is presented the From Features to Framework Tool (F3T),\nwhich supports the use of the F3 approach on framework development.\nResults: This tool provides an editor for domain modeling and generates framework\nsource-code according to the patterns of the F3 approach. In addition, F3T also\ngenerates a Domain-Specific Modeling Language that allows the modeling of\napplications and the generation of their source-code. F3T has been evaluated in two\nexperiments and the results are presented in this article.\nConclusions: F3T facilitates framework development and reuse by omitting\nimplementation complexities and performing code generation....
Object-oriented technology has widely accepted as the preferred way for large as well as small-scale system design. By using this advanced technology we can develop software product of lower maintenance cost and higher quality. It is clear that the available traditional software metrics are insufficient for case of object-oriented software. As a result of that, a set of new object oriented software metrics came into existence.The software test metrics which are measure of quality of the code, are very important elements of quality driven testing. They help in assuring that whether the software is appropriate or not. Result shows, the best metrics suite which covers almost all the software quality assurance features is CK metrics. Measurement of structural quality of code is necessity for ensuring overall quality of the code. Cost of maintaining and developing software is highly affected by quality and complexity of the software. In this research paper we look into several object oriented metrics as well as general code quality metrics proposed by various well known researchers. These object oriented metrics and general code quality metrics are then applied to several java programs to analyze the structural code quality of software product. Software reliability, readability, maintainability, reusability can be calculated based on the values of such metrics. These results are used for improving overall quality of the code and helping in reduction of maintenance cost....
We present a review of the historical evolution of software engineering, intertwining it with the history of knowledge engineering\nbecause ââ?¬Å?those who cannot remember the past are condemned to repeat it.ââ?¬Â This retrospective represents a further step forward to\nunderstanding the current state of both types of engineerings; history has also positive experiences; some of them we would like to\nremember and to repeat. Two types of engineerings had parallel and divergent evolutions but following a similar pattern.We also\ndefine a set of milestones that represent a convergence or divergence of the software development methodologies. These milestones\ndo not appear at the same time in software engineering and knowledge engineering, so lessons learned in one discipline can help\nin the evolution of the other one....
Background: Due to the characteristics of the maintenance process followed in open\nsource systems, developers are usually overwhelmed with a great amount of bugs. For\ninstance, in 2012, approximately 7,600 bugs/month were reported for Mozilla systems.\nImproving developers� productivity in this context is a challenging task. In this paper,\nwe describe and evaluate the new version of NextBug, a tool for recommending similar\nbugs in open source systems. NextBug is implemented as a Bugzilla plug-in and it was\ndesign to help maintainers to select the next bug he/she would fix.\nResults: We evaluated the new version of NextBug using a quantitative and a\nqualitative study. In the quantitative study, we applied our tool to 130,495 bugs\nreported for Mozilla products, and we consider as similar bugs that were handled by\nthe same developer. The qualitative study reports the main results we received from a\nsurvey conducted with Mozilla developers and contributors. Most surveyed developers\nstated their interest in working with a tool like NextBug.\nConclusion: We achieved the following results in our evaluation: (i) NextBug was able\nto provide at least one recommendation to 65% of the bugs in the quantitative study,\n(ii) in 54% of the cases there was at least one recommendation among the top-3 that\nwas later handled by the same developer; (iii) 85% of Mozilla developers stated that\nNextBug would be useful to the Mozilla community....
Many reverse engineering techniques for data structures rely on the knowledge\nof memory allocation routines. Typically, they interpose on the system�s malloc and\nfree functions, and track each chunk of memory thus allocated as a data structure. However,\nmany performance-critical applications implement their own custom memory allocators. Examples\ninclude webservers, database management systems, and compilers like gcc and clang. As\na result, current binary analysis techniques for tracking data structures fail on such binaries.\nWe present MemBrush, a new tool to detect memory allocation and deallocation functions in\nstripped binaries with high accuracy.We evaluated the technique on a large number of real world\napplications that use custom memory allocators.We demonstrate that MemBrush can detect allocators/\ndeallocators with a high accuracy which is 52 out of 59 for allocators, and 29 out of 31 for\ndeallocators in SPECINT 2006. As we show, we can furnish existing reverse engineering tools\nwith detailed information about the memory management API, and as a result perform an analysis\nof the actual application specific data structures designed by the programmer. Our system\nuses dynamic analysis and detects memory allocation and deallocation routines by searching for\nfunctions that comply with a set of generic characteristics of allocators and deallocators....
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