International Journal of Software Engineering and Knowledge Engineering

Regression testing is a very time-consuming and expensive testing activity. Many test case prioritization techniques have been proposed to speed up regression testing. Previous studies show that no one technique is always best. Random strategy, as the simplest strategy, is not always so bad. Particularly, when a test suite has higher fault detection capability, the strategy can generate a better result. Nevertheless, due to the randomness, the strategy is not always as satisfactory as expected. In this context, we present a test case prioritization approach using fixed size candidate set adaptive random testing algorithm to reduce the effect of randomness and improve fault detection effectiveness. The distance between pair-wise test cases is assessed by exclusive OR. We designed and conducted empirical studies on eight C programs to validate the effectiveness of the proposed approach. The experimental results, confirmed by a statistical analysis, indicate that the approach we proposed is more effective than random and the total greedy prioritization techniques in terms of fault detection effectiveness. Although the presented approach has comparable fault detection effectiveness to ART-based and the additional greedy techniques, the time cost is much lower. Consequently, the proposed approach is much more cost-effective.

Software development in project teams has become more and more complex, with increasing demands for information and decision making. Software development in projects also hugely depends on effective interaction between people, and human factors have been identified as key to successful software projects. Especially in this context, managing and analyzing social networks is highly important. The instrument of social network analysis (SNA) provides fine-grained methods for analyzing social networks in project teams, going beyond the traditional tools and techniques of project management. This paper examines the importance of the application of SNA in software development projects. We conducted a systematic literature review (SLR) of research on software development projects and social network data published between 1980 and 2019. We identified and analyzed 86 relevant studies, finding that research on software development projects spans the topics of project organization, communication management, knowledge management, version and configuration management, requirement management, and risk management. Further, we show that most studies focus on project organization and that the most common method used to gather social data relies on automated extraction from various software development repositories in the SNA context. Our paper contributes to the software development literature by providing a broad overview of published studies on the use of social networks in helping software development projects. Finally, we identify research opportunities and make suggestions for addressing existing research gaps.

To ensure the quality of current highly configurable software systems, intensive testing is needed to test all the configuration combinations and detect all the possible faults. This task becomes more challenging for most modern software systems when constraints are given for the configurations. Here, intensive testing is almost impossible, especially considering the additional computation required to resolve the constraints during the test generation process. In addition, this testing process is exhaustive and time-consuming. Combinatorial interaction strategies can systematically reduce the number of test cases to construct a minimal test suite without affecting the effectiveness of the tests. This paper presents a new efficient search-based strategy to generate constrained interaction test suites to cover all possible combinations. The paper also shows a new application of constrained interaction testing in software fault searches. The proposed strategy initially generates the set of all possible $t$-$tuple$ combinations; then, it filters out the set by removing the forbidden $t$-$tuples$ using the Base Forbidden Tuple (BFT) approach. The strategy also utilizes a mixed neighborhood tabu search (TS) to construct optimal or near-optimal constrained test suites. The efficiency of the proposed method is evaluated through a comparison against two well-known state-of-the-art tools. The evaluation consists of three sets of experiments for 35 standard benchmarks. Additionally, the effectiveness and quality of the results are assessed using a real-world case study. Experimental results show that the proposed strategy outperforms one of the competitive strategies, ACTS, for approximately 83% of the benchmarks and achieves similar results to CASA for 65% of the benchmarks when the interaction strength is 2. For an interaction strength of 3, the proposed method outperforms other competitive strategies for approximately 60% and 42% of the benchmarks. The proposed strategy can also generate constrained interaction test suites for an interaction strength of 4, which is not possible for many strategies. The real-world case study shows that the generated test suites can effectively detect injected faults using mutation testing.

There has been observed explosive growth in the development of mobile applications (apps) for Android and iOS operating systems, which has led to the direct impact towards mobile app development. In order to design and propose quality-oriented apps, it is the primary responsibility of developers to devote time and sufficient efforts towards testing to make the apps bug-free and operational in the hands of end-users without any hiccup. Manual testing procedures take a prolonged amount of time in writing test cases, and in some cases, the full testing requirements are not met. Besides, the insufficient knowledge of tester also impacts the overall quality and bug-free apps. To overcome the obstacles of testing, we propose a new testing methodology cum tool called “AgileUATM” which works primarily towards white-box and black-box testing. To evaluate the validity of the proposed tool, we put the tool in a real-time operational environment concerning mobile test apps. By using this tool, all the acceptance criteria are determined via user stories. The testers/developers specify requirements with formal specifications based on programs properties, predicates, invariants, and constraints. The results show that the proposed tool generated effective and accurate test cases, test input. Meanwhile, expected output was also generated in a unified fashion from the user stories to meet acceptance criteria. The proposed solution also reduced the development time to identify test data as compared to manual Behavior-Driven Development (BDD) methodologies. This tool can support the developers to get a better idea about the required tests and able to translate the customer’s natural languages to computer languages as well. This paper fulfills an approach to suitably test mobile application development.

Besides the applications aimed at increasing the efficiency of the Android mobile devices, also many malicious applications, millions of Android malware according to various security company reports, are being developed and uploaded into the application stores. In order to detect those applications, a malicious Android application detection system based on permission and permission groups namely, AppPerm Analyzer has been developed. The AppPerm Analyzer software extracts the manifest and code permissions of analyzed applications, creates duple and triple permission groups from them, calculates risk scores of these permissions and permission groups according to their usage rates in malicious and benign applications and calculates the total risk score of the analyzed application. After training the software with 7776 applications in total, it is tested with 1664 benign and 1664 malicious applications. In the tests, AppPerm Analyzer detected malicious applications with an accuracy of 96.19% at most. At this point, sensitivity (true-positive ratio) is 95.50% and specificity (true-negative ratio) is 96.88%. If a false-positive ratio up to 10% is accepted, the sensitivity increases to 99.04%.

Generating faulty data is a key issue in fault injection. The faulty data include not only the ones of extreme values or bad formats, but also the ones which are logically unreasonable. Constraint-based fault injection which negates interface constraints to solve faulty data is effective for logically unreasonable data generation. However, the existing constraint-based approaches just solve brand new data for testing. Such brand new data may easily violate some hidden environment constraints on the test inputs and hence be nonrealistic. Besides, there can be different strategies to negate a constraint in order to derive the constraint-unsatisfied faulty data. What are the possible negation strategies and which strategies are better for high coverage fault injection are still unclear. To these ends, this paper presents a new constraint-based fault injection approach. The approach introduces 10 different strategies for constraint negation and relaxes constraint variables to generate faulty data instead of solving brand new data for fault injection. It can produce faulty data which are closer to the original non-faulty ones and hence likely to be more realistic. We experimentally investigated the effectiveness and cost of the introduced constraint negation strategies. The results provide insights for the application of these strategies in fault injection.