The software industry has rapidly expanded, and software development is now essential to the success of many multinational corporations. The demand for complex software systems has also dramatically increased. Effective software development is essential, given the limitations of resources such as money, time, and labor. The cost and effort calculations affect the development process and client needs. Project failure was usually caused by errors in job estimating. Underestimating could have severe repercussions, if the project’s budget ends up being more than anticipated. Project overruns, on the other hand, can also have a detrimental impact on how successfully software projects are finished. Researchers and experts in the field of software are investigating ways to keep management and development productivity at high levels. However, a stand-alone estimation has not produced any noteworthy research results. Over the last decade, the standalone estimating models have revealed inadequacies. Literature suggests opting ensemble models, would yield better results as compared to standalone models. We have proposed a heterogeneous ensemble effort estimation (EEE) model in this research. The proposed model is comprises of standalone estimating models such as 1) Use Case Point, 2) Expert Judgment (EJ) and 3)Artificial neural network (ANN). Finally, using linear combination rules, the effort of each unique base model is combined. We applied our ensemble model on the benchmark dataset i.e. ISBSG using three different variations to avoid biasness. Furthermore, trained models were applied on industry use-cases for cross-validation. The findings of this study demonstrated that, in comparison to stand-alone estimate strategies, the ensemble technique gave better estimation results.

This pilot study examines how students’ performance has evolved in an Object-oriented (OO) programming course and contributes to the learning analytic framework for similar programming courses in university curriculum. First, we briefly introduce the research background, a novel OO teaching practice with consecutive and iterative assignments consisting of programming and testing assignments. We propose a planned quantitative method for assessing students’ gains in terms of programming performance and testing performance. Based on real data collected from students who engaged in our course, we use trend analysis to observe how students’ performance has improved over the whole semester. By using correlation analysis, we obtain some interesting findings on how students’ programming performance correlates with testing performance, which provides persuasive empirical evidence in integrating software testing practices into an Object-oriented programming curriculum. Then, we conduct an empirical study on how students’ design competencies are represented by their program code quality changes over consecutive assignments by analyzing their submitted source code in the course course system and the GitLab repository. Three different kinds of profiles are found in the students’ program quality in the OO design level. The group analysis results reveal several significant differences in their programming performance and testing performance. Moreover, we conduct systematical explanations on how students’ programming skill improvement can be attributed to their object-oriented design competency. By performing principal component analysis on software statistical data, a predictive OO metrics suite for both students’ programming performance and their testing performance is proposed. The results show that these quality factors can serve as useful predictors of students’ learning performance and can provide effective feedback to the instructors in the teaching practices.

Regression testing is required to assure the quality of each iteration of microservice systems. Test case selection is one of main techniques to optimize regression testing. Existing techniques mainly involve artifacts acquisition, processing and maintenance, thus hard to apply in microservice regression testing since it is difficult to obtain and process required artifacts from multiple development teams, which is normal in cases of microservice systems. This paper proposes a novel approach, namely MRTS-BP, which takes API gateway logs instead of artifacts as inputs. By mining service dependencies from API gateway logs, MRTS-BP analyzes service change impacts based on a propagation calculation, and selects test cases affected by changes based on impact degree values. To evaluate the effectiveness of MRTS-BP, empirical studies based on four real deployed systems are presented. Retest-all strategy and a regression testing selection approach based on control flow graphs called RTS-CFG are compared with MRTS-BP. The results show that, MRTS-BP can significantly reduce both the number of test cases and overall time cost while maintaining the fault detection capability of selected test suite, and that MRTS-BP can save more time cost than RTS-CFG with the similar safety and precision.

Numerous factors have an impact on the temporal correction of integrated modular avionics application. As early as in the design phase, verification should be conducted to guarantee the rigorous temporal requirements for safety consideration which are fulfilled. This paper proposes a model for underlining verification in the design phase. The model captures the temporally related design in an industrial standard for software application and operation system (ARINC653). The finite-state machine mechanism is employed to formulate the behavior model of the application and application execution interface. Furthermore, temporal requirements are measured under the simulation of the state machine model. A case study is conducted on a real-world auto-pilot application that conformed with ARINC653.