软件工程 | CCF推荐SCI期刊专刊信息2条

全文截稿: 2020-11-02

影响因子: 1.088

CCF分类: B类

中科院JCR分区:

• 大类 : 工程技术 - 4区

• 小类 : 计算机：软件工程 - 4区

网址: http://www.journals.elsevier.com/science-of-computer-programming/

ECOOP is Europe's longest-standing annual Programming Languages (PL) conference. Originally its primary focus was on object orientation, but now it looks at a much broader range of programming topics. For the 2021 edition of ECOOP we are pleased to announce a special issue of Science of Computer Programming. For papers appearing in this special Science of Computer Programming issue, the authors will be invited to present their work at ECOOP, and a short abstract will also appear in the main ECOOP conference proceedings.

Areas of interest include the design, implementation, optimization, analysis, testing, verification, and theory of programs, programming languages, and programming environments. Papers may focus on new tools (e.g., language implementations, program analyses, runtime systems), techniques (e.g., code organization approaches, methodologies), principles (e.g., semantics, proofs, paradigms), evaluations (e.g., experiments, corpora analyses, user studies), or some combination of the above. Papers presenting thorough evaluations of existing solutions that provide new insights are welcome. We also encourage the submission of reproduction studies.

全文截稿: 2021-03-31

影响因子: 2.559

CCF分类: B类

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 计算机：软件工程 - 2区

• 小类 : 计算机：理论方法 - 3区

网址: http://www.journals.elsevier.com/journal-of-systems-and-software/

Today software has a significant impact on all aspects of our society, and its functioning is crucial for a multitude of economic, social, and educational activities. As a consequence, assuring the correctness and quality of software systems and components becomes paramount.

Automation across the Software-Testing Process is a powerful asset: while originally conceived for test execution, nowadays it is increasingly used for test generation, test prioritization and selection, test repair and evolution, among others, as well as for automatically comparing the actual with the expected outcome. Investments on automation of both tests synthesis and their execution are pursued to help ensure the adequate quality of software systems/applications while reducing the high effort and costs incurred in the testing of complex systems. De-facto, test automation is central in many modern approaches to software development such as model-driven engineering, agile frameworks, or TDD (Test Driven Development). It also represents a cornerstone for evolutionary development life-cycle focusing on “Continuous Practices” (i.e., continuous integration, testing, delivery, and deployment), or more recently on DevOps.

On the other hand, as with any piece of software, development and maintenance of test code require considerable effort and skills. Besides, tests themselves need to be kept aligned with the ever-evolving system/application under test. Therefore, the potential benefits aimed by test automation have to be weighted against its costs and drawbacks.

In today's competitive world, companies demand a good Return On Investment (ROI) for every task concerning software development, and software testing is no exception. And in complex technical endeavours such as software testing, calculation of investments and returns cannot be reduced to just a balancing of money flow, but should also consider other non-financial, yet very concrete and critical aspects, such as social acceptance, developer's reputation, security and privacy, etc.

On the one side, research in test automation is very active, and in the last years, several solutions and tools have emerged that claim to improve the cost/effectiveness of testing. For instance, design patterns used for structuring test code, refactoring strategies and tools helping in development and maintenance. However, we are not yet able to foresee the investment costs versus money saved when a proposed test automation solution is introduced in a Company.

On the other side, managers demand evidence that proposed solutions actually improve the ROI and need applicable metrics to estimate the concrete costs/benefits of test automation. They would also need support in deciding which solution is best for their context, and often this is impeded by the large gap between an academic solution and the many concrete issues encountered in transferring it to practice, e.g., lack of scalability, flakiness, tight time-to-market. Moreover, inadequate testing and wrong decisions in test automation may be clearly the cause of a technical debt, and being able to reason on costs and benefits of test automation solutions can help prevent and reduce it.