In this paper we promote introducing software verification and control flow graph similarity measurement in automated evaluation of students' programs. We present a new grading framework that merges results obtained by combination of these two approaches with results obtained by automated testing, leading to improved quality and precision of automated grading. These two approaches are also useful in providing a comprehensible feedback that can help students to improve the quality of their programs We also present our corresponding tools that are publicly available and open source. The tools are based on LLVM low-level intermediate code representation, so they could be applied to a number of programming languages. Experimental evaluation of the proposed grading framework is performed on a corpus of university students' programs written in programming language C. Results of the experiments show that automatically generated grades are highly correlated with manually determined grades suggesting that the presented tools can find real-world applications in studying and grading.
Software verification techniques are not commonly used in automated evaluation of programs. There are limited experiments on using Java PathFinder model checker for automated test case generation @cite_35 . Tools with integrated support for automated testing and verification, e.g. Ceasar @cite_34 , are usually too complex and not aimed for educational purposes. To the authors' knowledge, there is no other software verification tool deployed in process of automated bug finding as a complement to automated testing of students' programs. The tool LAV was already used, to a limited extent, for finding bugs in students' programs @cite_26 . In that work, a different sort of corpus was used, as discussed in Section . Also, that application did not aim at automated grading, and instead was made in the wider context of design and development of LAV as a general-purpose SMT-based error finding platform.
@cite_35 @cite_34 @cite_26
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We present design and evaluation of LAV, a new open-source tool for statically checking program assertions and errors. LAV integrates into the popular LLVM infrastructure for compilation and analysis. LAV uses symbolic execution to construct a first-order logic formula that models the behavior of each basic blocks. It models the relationships between basic blocks using propositional formulas. By combining these two kinds of formulas LAV generates polynomial-sized verification conditions for loop-free code. It uses underapproximating or overapproximating unrolling to handle loops. LAV can pass generated verification conditions to one of the several SMT solvers: Boolector, MathSAT, Yices, and Z3. Our experiments with small 200 benchmarks suggest that LAV is competitive with related tools, so it can be used as an effective alternative for certain verification tasks. The experience also shows that LAV provides significant help in analyzing student programs and providing feedback to students in everyday university practice. Software testing remains the most widely used approach to verification in industry today, consuming between 30-50 percent of the entire development cost. Test input selection for intelligent agents presents a problem due to the very fact that the agents are intended to operate robustly under conditions which developers did not consider and would therefore be unlikely to test. Using methods to automatically generate and execute tests is one way to provide coverage of many conditions without significantly increasing cost. However, one problem using automatic generation and execution of tests is the oracle problem: How can we automatically decide if observed program behavior is correct with respect to its specification? In this paper, we present a model-based oracle generation method for unit testing belief-desire-intention agents. We develop a fault model based on the features of the core units to capture the types of faults that may be encountered and define how to automatically generate a partial, passive oracle from the agent design models. We evaluate both the fault model and the oracle generation by testing 14 agent systems. Over 400 issues were raised, and these were analyzed to ascertain whether they represented genuine faults or were false positives. We found that over 70 percent of issues raised were indicative of problems in either the design or the code. Of the 19 checks performed by our oracle, faults were found by all but 5 of these checks. We also found that 8 out the 11 fault types identified in our fault model exhibited at least one fault. The evaluation indicates that the fault model is a productive conceptualization of the problems to be expected in agent unit testing and that the oracle is able to find a substantial number of such faults with relatively small overhead in terms of false positives. Automated program repair can potentially reduce debugging costs and improvesoftware quality but recent studies have drawn attention to shortcomings inthe quality of automatically generated repairs. We propose a new kind ofrepair that uses the large body of existing open-source code to findpotential fixes. The key challenges lie in efficiently finding codesemantically similar (but not identical) to defective code and thenappropriately integrating that code into a buggy program. We presentSearchRepair, a repair technique that addresses these challenges by(1) encoding a large database of human-written code fragments as SMTconstraints on input-output behavior, (2) localizing a given defect to likelybuggy program fragments and deriving the desired input-output behavior forcode to replace those fragments, (3) using state-of-the-art constraintsolvers to search the database for fragments that satisfy that desiredbehavior and replacing the likely buggy code with these potential patches, and (4) validating that the patches repair the bug against program testsuites. We find that SearchRepair repairs 150 (19 ) of 778 benchmark Cdefects written by novice students, 20 of which are not repaired by GenProg, TrpAutoRepair, and AE. We compare the quality of the patches generated by thefour techniques by measuring how many independent, not-used-during-repairtests they pass, and find that SearchRepair-repaired programs pass 97.3 ofthe tests, on average, whereas GenProg-, TrpAutoRepair-, and AE-repairedprograms pass 68.7 , 72.1 , and 64.2 of the tests, respectively. We concludethat SearchRepair produces higher-quality repairs than GenProg, TrpAutoRepair, and AE, and repairs some defects those tools cannot. Reviews studies from 1991-2013 to assess application of ML techniques for SFP.Identifies seven categories of the ML techniques.Identifies 64 studies to answer the established research questions.Selects primary studies according to the quality assessment of the studies.Systematic literature review performs the following:Summarize ML techniques for SFP models.Assess performance accuracy and capability of ML techniques for constructing SFP models.Provide comparison between the ML and statistical techniques.Provide comparison of performance accuracy of different ML techniques.Summarize the strength and weakness of the ML techniques.Provides future guidelines to software practitioners and researchers. BackgroundSoftware fault prediction is the process of developing models that can be used by the software practitioners in the early phases of software development life cycle for detecting faulty constructs such as modules or classes. There are various machine learning techniques used in the past for predicting faults. MethodIn this study we perform a systematic review of studies from January 1991 to October 2013 in the literature that use the machine learning techniques for software fault prediction. We assess the performance capability of the machine learning techniques in existing research for software fault prediction. We also compare the performance of the machine learning techniques with the statistical techniques and other machine learning techniques. Further the strengths and weaknesses of machine learning techniques are summarized. ResultsIn this paper we have identified 64 primary studies and seven categories of the machine learning techniques. The results prove the prediction capability of the machine learning techniques for classifying module class as fault prone or not fault prone. The models using the machine learning techniques for estimating software fault proneness outperform the traditional statistical models. ConclusionBased on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers. However, the application of the machine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.