Model compilation: An approach to automated model derivation

An approach is introduced to automated model derivation for knowledge based systems. The approach, model compilation, involves procedurally generating the set of domain models used by a knowledge based system. With an implemented example, how this approach can be used to derive models of different precision and abstraction is illustrated, and models are tailored to different tasks, from a given set of base domain models. In particular, two implemented model compilers are described, each of which takes as input a base model that describes the structure and behavior of a simple electromechanical device, the Reaction Wheel Assembly of NASA's Hubble Space Telescope. The compilers transform this relatively general base model into simple task specific models for troubleshooting and redesign, respectively, by applying a sequence of model transformations. Each transformation in this sequence produces an increasingly more specialized model. The compilation approach lessens the burden of updating and maintaining consistency among models by enabling their automatic regeneration

Spectrum-Based Fault Localization in Model Transformations

Model transformations play a cornerstone role in Model-Driven Engineering (MDE), as they provide the essential mechanisms for manipulating and transforming models. The correctness of software built using MDE techniques greatly relies on the correctness of model transformations. However, it is challenging and error prone to debug them, and the situation gets more critical as the size and complexity of model transformations grow, where manual debugging is no longer possible. Spectrum-Based Fault Localization (SBFL) uses the results of test cases and their corresponding code coverage information to estimate the likelihood of each program component (e.g., statements) of being faulty. In this article we present an approach to apply SBFL for locating the faulty rules in model transformations. We evaluate the feasibility and accuracy of the approach by comparing the effectiveness of 18 different stateof- the-art SBFL techniques at locating faults in model transformations. Evaluation results revealed that the best techniques, namely Kulcynski2, Mountford, Ochiai, and Zoltar, lead the debugger to inspect a maximum of three rules to locate the bug in around 74% of the cases. Furthermore, we compare our approach with a static approach for fault localization in model transformations, observing a clear superiority of the proposed SBFL-based method.Comisión Interministerial de Ciencia y Tecnología TIN2015-70560-RJunta de Andalucía P12-TIC-186

MintHint: Automated Synthesis of Repair Hints

Being able to automatically repair programs is an extremely challenging task. In this paper, we present MintHint, a novel technique for program repair that is a departure from most of today's approaches. Instead of trying to fully automate program repair, which is often an unachievable goal, MintHint performs statistical correlation analysis to identify expressions that are likely to occur in the repaired code and generates, using pattern-matching based synthesis, repair hints from these expressions. Intuitively, these hints suggest how to rectify a faulty statement and help developers find a complete, actual repair. MintHint can address a variety of common faults, including incorrect, spurious, and missing expressions. We present a user study that shows that developers' productivity can improve manyfold with the use of repair hints generated by MintHint -- compared to having only traditional fault localization information. We also apply MintHint to several faults of a widely used Unix utility program to further assess the effectiveness of the approach. Our results show that MintHint performs well even in situations where (1) the repair space searched does not contain the exact repair, and (2) the operational specification obtained from the test cases for repair is incomplete or even imprecise

Assessment and improvement of automated program repair mechanisms and components

2015 Spring.Includes bibliographical references.Automated program repair (APR) refers to techniques that locate and fix software faults automatically. An APR technique locates potentially faulty locations, then it searches the space of possible changes to select a program modification operator (PMO). The selected PMO is applied to a potentially faulty location thereby creating a new version of the faulty program, called a variant. The variant is validated by executing it against a set of test cases, called repair tests, which is used to identify a repair. When all of the repair tests are successful, the variant is considered a potential repair. Potential repairs that have passed a set of regression tests in addition to those included in the repair tests are deemed to be validated repairs. Different mechanisms and components can be applied to repair faults. APR mechanisms and components have a major impact on APR effectiveness, repair quality, and performance. APR effectiveness is the ability to and potential repairs. Repair quality is defined in terms of repair correctness and maintainability, where repair correctness indicates how well a potential repaired program retains required functionality, and repair maintainability indicates how easy it is to understand and maintain the generated potential repair. APR performance is the time and steps required to find a potential repair. Existing APR techniques can successfully fix faults, but the changes inserted to fix faults can have negative consequences on the quality of potential repairs. When a potential repair is executed against tests that were not included in the repair tests, the "repair" can fail. Such failures indicate that the generated repair is not a validated repair due to the introduction of other faults or the generated potential repair does not actually fix the real fault. In addition, some existing techniques add extraneous changes to the code that obfuscate the program logic and thus reduce its maintainability. APR effectiveness and performance can be dramatically degraded when an APR technique applies many PMOs, uses a large number of repair tests, locates many statements as potentially faulty locations, or applies a random search algorithm. This dissertation develops improved APR techniques and tool set to help optimize APR effectiveness, the quality of generated potential repairs, and APR performance based on a comprehensive evaluation of APR mechanisms and components. The evaluation involves the following: (1) the PMOs used to produce repairs, (2) the properties of repair tests used in the APR, (3) the fault localization techniques employed to identify potentially faulty statements, and (4) the search algorithms involved in the repair process. We also propose a set of guided search algorithms that guide the APR technique to select PMO that fix faults, which thereby improve APR effectiveness, repair quality, and performance. We performed a set of evaluations to investigate potential improvements in APR effectiveness, repair quality, and performance. APR effectiveness of different program modification operators is measured by the percent of fixed faults and the success rate. Success rate is the percentage of trials that result in potential repairs. One trial is equivalent to one execution of the search algorithm. APR effectiveness of different fault localization techniques is measured by the ability of a technique to identify actual faulty statements, and APR effectiveness of various repair test suites and search algorithms is also measured by the success rate. Repair correctness is measured by the percent of failed potential repairs for 100 trials for a faulty program, and the average percent of failed regression tests for N potential repairs for a faulty program; N is the number of potential repairs generated for 100 trials. Repair maintainability is measured by the average size of a potential repair, and the distribution of modifications throughout a potential repaired program. APR performance is measured by the average number of generated variants and the average total time required to find potential repairs. We built an evaluation framework creating a configurable mutation-based APR (MUT-APR) tool. MUT-APR allows us to vary the APR mechanisms and components. Our key findings are the following: (1) simple PMOs successfully fix faulty expression operators and improve the quality of potential repairs compared to other APR techniques that use existing code to repair faults, (2) branch coverage repair test suites improve APR effectiveness and repair quality significantly compared to repair test suites that satisfy statement coverage or random testing; however, they lowered APR performance, (3) small branch coverage repair test suites improved APR effectiveness, repair quality, and performance significantly compared to large branch coverage repair tests, (4) the Ochiai fault localization technique always identifies seeded faulty statements with an acceptable performance, and (5) guided random search algorithm improves APR effectiveness, repair quality, and performance compared to all other search algorithms; however, the exhaustive search algorithms is guaranteed a potential repair that failed fewer regression tests with a significant performance degradation as the program size increases. These improvements are incorporated into the MUT-APR tool for use in program repairs

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial