Inline tests validate individual program statements and expressions, and they detect many seeded faults (i.e., mutants) that unit tests miss in these target statements. ExLi is the only automated inline-test generation technique today; it carves inline tests from method-level unit tests that are written for methods that enclose target statements. Thus, ExLi cannot work for target statements that method-level unit tests do not cover. Also, the quality of ExLi-generated inline tests depends on the quality of method-level unit tests. We propose Smack to generate inline tests without relying on method-level unit tests. Smack is inspired by how inline tests are run: each is extracted with the target statement and run independently. Smack exploits this independence. Smack first extracts each target statement into a new method. Next, Smack applies a unit-test generator to the extracted method, which tends to have simpler control flow than the target statement’s enclosing method and carves inline tests from them. Finally, Smack transplants the resulting inline tests to be right after the target statement in the original enclosing method. We evaluate Smack on the same 957 target statements in 31 open-source projects that ExLi was evaluated on. Smack generates inline tests for 277 of 312 (88.8%) statements that ExLi cannot handle. These inline tests kill 96.7% of 1,815 mutants in these 312 target statements. Therefore, Smack improves on ExLi by extending the reach and fault-detection ability of inline-test generation. Smack also generates inline tests for 609 of 645 (94.4%) target statements that ExLi can handle. Smack-generated inline tests kill 83.1% of 2,844 mutants in these 645 target statements. 147 of these killed mutants survive ExLi-generated inline tests. So, Smack is also complementary to ExLi on target statements that ExLi can handle.

We recently proposed inline tests for validating individual program statements; they allow developers to provide test inputs, expected outputs, and test oracles immediately after a target statement. But, existing code can have many target statements. So, automatic generation of inline tests is an important next step towards increasing their adoption. We propose ExLi, the first technique for automatically generating inline tests. ExLi extracts inline tests from unit tests; it first records all variable values at a target statement while executing unit tests. Then, ExLi uses those values as test inputs and test oracles in an initial set of generated inline tests. Target statements that are executed many times could have redundant initial inline tests. So, ExLi uses a novel coverage-then-mutants based reduction process to remove redundant inline tests. We implement ExLi for Java and use it to generate inline tests for 718 target statements in 31 open-source programs. ExLi reduces 17,273 initially generated inline tests to 905 inline tests. The final set of generated inline tests kills up to 25.1% more mutants on target statements than developer written and automatically generated unit tests. That is, ExLi generates inline tests that can improve the fault-detection capability of the test suites from which they are extracted.

Conducting valid statistical analyses is challenging in the presence of missing-not-at-random (MNAR) data, where the missingness mechanism is dependent on the missing values themselves even conditioned on the observed data. Here, we consider a MNAR model that generalizes several prior popular MNAR models in two ways: first, it is less restrictive in terms of statistical independence assumptions imposed on the underlying joint data distribution, and second, it allows for all variables in the observed sample to have missing values. This MNAR model corresponds to a so-called criss-cross structure considered in the literature on graphical models of missing data that prevents nonparametric identification of the entire missing data model. Nonetheless, part of the complete-data distribution remains nonparametrically identifiable. By exploiting this fact and considering a rich class of exponential family distributions, we establish sufficient conditions for identification of the complete-data distribution as well as the entire missingness mechanism. We then propose methods for testing the independence restrictions encoded in such models using odds ratio as our parameter of interest. We adopt two semiparametric approaches for estimating the odds ratio parameter and establish the corresponding asymptotic theories: one involves maximizing a conditional likelihood with order statistics and the other uses estimating equations. The utility of our methods is illustrated via simulation studies.