Exploring all the feasible paths in order to generate test cases is costly when dynamic symbolic execution is considered. Hence, there comes the interpolation concept that minimizes the cost to some extent. The earlier work on custom interpolation introduced a resource annotator that instruments the program and generates multiple meta programs (LLVM IRs) in order to generate optimal MCD/DC-based test cases. The proposed approach leverages a Meta Program Generator (MPG) to create a single meta program that encapsulates SC-MCC sequences within “assert” statements, aligned with their corresponding predicates. The effectiveness of this approach is demonstrated through experiments on benchmark programs, comparing it with traditional methods. The results indicate improved efficiency and the generation of a higher number of feasible SC-MCC sequences, making our approach a promising advancement in software testing and symbolic execution. We experimented with 75 Rigorous Examination of Reactive Systems (RERS) benchmark programs for experimentation. It is observed that our implementation has obtained more feasible SC-MCC sequences in 41 out of 75 programs.

Unique-Cause MC/DC (UCM) is the most desired form of MC/DC in many safety-critical applications. For a given predicate, the UCM considers the independent pair of each condition by flipping the corresponding condition and fixing the other conditions. For the given N conditions in a predicate, the existing static symbolic execution tool, CBMC, generates MC/DC (Modified Condition/Decision) goal constraints of size, N + 1 that constitutes its minimal independent pairs. However, we propose a novel UCM Sequence Generator (UCM-Gen) that generates all possible inequality comparisons of the sequences/combinations of the N conditions which helps in computing the independent pairs further. The UCM-Gen outputs UCM Annotated Program which when given to the program verifiers, produces the UCM Score (%). In our work, we have considered CBMC to get the SAT/UNSAT results for each sequence of the UCM Annotated Program. Upon analysing these results, we calculate the total number of independently affected conditions (i.e., I value) for all the predicates in the given program. Furthermore, this work is compared with the CBMC's mode of MC/DC implementation. Interestingly, our proposed approach based UCM score (%) is always greater than the CBMC's MC/DC score (%) and hence claiming that their corresponding test cases contribute in effective bug finding.

The important contribution that large language models (LLMs) have made to the development of a new software testing era is the main objective of this proposed approach. It emphasizes the role that LLMs play in producing complex and diverse input seeds, which opens the way for efficient bug discovery. In the study we also introduce a systematic approach for combining various input values, employing the principles of Combinatorial testing using the PICT (Pairwise independent Combinatorial testing). By promoting a more varied set of inputs for thorough testing, PICT enhances the seed production process. Then we show how these different seeds may be easily included in the American Fuzzy Lop (AFL) tool, demonstrating how AFL can effectively use them to find and detect software flaws. This integrated technique offers a powerful yet straightforward approach to software Quality.

Modified Condition/Decision Coverage (MC/DC) is an important criterion to test the safety-critical applications because it generates test cases in a linear manner from N + 1 to 2 N , where N is the number of Atomic Conditions in a given predicate. It is desirable in comparison to the exponential test cases produced for Multiple Condition Coverage (MCC), i.e., 2 N . However, MCC with Short-Circuit (SC-MCC) is recommended since most of the safety-critical applications are built on the high-level languages that use Short-Circuit evaluation property. Our goal is to demonstrate that, despite the added overhead, the SC-MCC coverage-based test cases have a high error-detection probability compared to that of MC/DC. In this work, we have considered the CBMC tool to generate both the SC-MCC and MC/DC test cases for 80 RERS benchmark programs . Then, by optimizing the traditional Mutation Testing (using the GCOV tool), we computed the Mutation Score (%) to assess the quality of the generated test cases . As per the Mutation analysis, the proposed SC-MCC outperformed MC/DC for over 75% of the 80 RERS programs considered. Additionally, as per the Execution time analysis, the average total time taken to evaluate the MC/DC part of the proposed framework is 3065.98 s, whereas SC-MCC framework evaluation took 2167.70 s. This proves the efficiency of the proposed (SC-MCC) work over the traditional criterion (MC/DC) based work.

In this paper, we propose and develop a real-time profiler to validate the strong coverage criteria such as Modified Condition/Decision Coverage (MC/DC) and Multiple Condition Coverage with Short Circuit (SC-MCC). We named our new tool as gMCov, which is a gCov based profiler. Currently, the existing gCov profiler gives line coverage and branch coverage. As we know, line and branch coverages are weak coverage criteria. Since there exists no profiler to produce the information of feasible sequence properties of either MC/DC or SC-MCC, it is important to have a tool that validates these coverage criteria based on their final scores. Hence, we proposed a new profiler i.e. gMCov which is a generalized tool that can be plugged with any test case generator. It requires a set of test cases and a program to produce the score (%) with a detailed report. The overhead of the gMCov execution time is considerable i.e., 0.68 (s) for MC/DC and 1.26 (s) for SC-MCC, thus proving its efficiency.