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Field corn, Zea mays L., is a commonly grown crop in Mississippi. Brown stink bug, Euschistus servus Say, is an insect that can infest field corn. Growers and consultants have expressed concerns of the difficulty in detecting infestations and estimating yield loss potential once damage is found in a field. The results of these experiments showed a relationship between damage severity, plant height, and yield loss. As damage severity increased, plant height and yield were significantly reduced. On a per area basis, yields were reduced when ≥ 10% plants were damaged. Mean plant heights were reduced when ≥ 20% plants were damaged. Results from simulated damage experiments were similar to those of the natural infestation damage; however, target damage severities (damage ratings) were not achieved. Further methodology refinement is needed.

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My dissertation and research focused on the effects of insecticides on squash bugs, its egg parasitoid, and pollinators in the production of cucurbits in Virginia. Plants in the cucumber family are dependent on insect pollination for successful fruit set, and are also susceptible to plant eating insects. Squash bugs are capable of transmitting cucurbit yellow vine decline, and their feeding can cause significant wilt and death in many varieties. To control for squash bug and other pests, growers commonly combine the application of broad-spectrum insecticides with the frequently applied prophylactic fungicides. Broad-spectrum insecticide applications are known to have negative effects on natural enemy populations, are capable of promoting insecticide resistance, and can have negative effects on pollinators if care in their use is not taken. Squash bugs have several natural enemies, but their predominant egg parasitoid is most effective at reducing damaging populations. The scelionid wasp Gryon pennsylvanicum Ashmead, is a prevalent egg parasitoid in Virginia and can be negatively affected by the application of broad-spectrum insecticides. Through survey efforts I found that G. pennsylvanicum is widely distributed throughout Virginia and is capable of high rates of egg parasitism (>90%). This is contrary to the 20% level previously assumed for the East Coast. I explored the effects of narrow-spectrum insecticides on the fate of the egg parasitoids, those developing in the host egg and emerged adults of G. pennsylvanicum. Contact assays showed that the insecticides λ-cyhalothrin and sulfoxaflor had caused high adult parasitoid mortality. As new insecticides get registered for use there is often concern about their effect on pollinators, specifically the European honey bee Apis mellifera L. I evaluated the use of large flight cages as a method to measure the sub-lethal effects of narrow-spectrum insecticides to honey bees, as a means to qualify risk. The method utilizes small colonies of honey bees (with stores of nectar and pollen) and their feeding at a treated sucrose solution after being trained to a feeder in an enclosed arena. This choice-test style behavioral experiment shows promise in qualifying the risks associated with insecticide exposure in the field. In the case of pyrifluquinazon, colonies repeatedly choose to avoid feeding at tainted feeders even after training with no other outside sources of food present. Further researching the sub-lethal behavioral effects that insecticides have on bees in a colony can help us better qualify their risk. / Ph. D.

Free/Open Source Software (FOSS) communities often use open bug reporting to allow users to participate by reporting bugs. This practice can lead to more duplicate reports, as inexperienced users can be less rigorous about researching existing bug reports. The purpose of this research is to determine the extent of this problem, and how FOSS projects deal with duplicate bug reports. We examined 12 FOSS projects: 4 small, 4 medium and 4 large, where size was determined by number of code contributors. First, we found that contrary to what has been reported from studies of individual large projects like Mozilla and Eclipse, duplicate bug reports are a problem for FOSS projects, especially medium-sized projects. These medium sized projects struggle with a large number of submissions and duplicates without the resources large projects use for dealing with these. Second, we found that the focus of a project does not affect the number of duplicate bug reports. Our findings point to a need for additional scaffolding and training for bug reporters of all types. Finally, we examine the impact that automatic crash reporting has on these bug repositories. These systems are quickly gaining in popularity and aim to help end-users submit vital bug information to the developers. These tools generate stack traces and memory dumps from software crashes and package these up so end-users can submit them to the project with a single mouse-click. We examined Mozilla's automatic crash reporting systems, Breakpad and Socorro, to determine how these integrate with the open bug reporting process, and whether they add to the confusion of duplicate bug reports. We found that though initial adoption exhibited teething troubles, these systems add significant value and knowledge, though the signal to noise ratio is high and the number of bugs identified per thousand reports is low. / Graduation date: 2012

Many bugs, even those that are known and documented in bug reports, remain in mature software for a long time due to the lack of the development resources to fix them. We propose a general approach, R2Fix, to automatically generate bug-fixing patches from free-form bug reports. R2Fix combines past fix patterns, machine learning techniques, and semantic patch generation techniques to fix bugs automatically. We evaluate R2Fix on three large and popular software projects, i.e., the Linux kernel, Mozilla, and Apache, for three important types of bugs: buffer overflows, null pointer bugs, and memory leaks. R2Fix generates 60 patches correctly, 5 of which are new patches for bugs that have not been fixed by developers yet. We reported all 5 new patches to the developers; 4 have already been accepted and committed to the code repositories. The 60 correct patches generated by R2Fix could have shortened and saved an average of 68 days of bug diagnosis and patch generation time.

No description available.

Many bugs, even those that are known and documented in bug reports, remain in mature software for a long time due to the lack of the development resources to fix them. We propose a general approach, R2Fix, to automatically generate bug-fixing patches from free-form bug reports. R2Fix combines past fix patterns, machine learning techniques, and semantic patch generation techniques to fix bugs automatically. We evaluate R2Fix on three large and popular software projects, i.e., the Linux kernel, Mozilla, and Apache, for three important types of bugs: buffer overflows, null pointer bugs, and memory leaks. R2Fix generates 60 patches correctly, 5 of which are new patches for bugs that have not been fixed by developers yet. We reported all 5 new patches to the developers; 4 have already been accepted and committed to the code repositories. The 60 correct patches generated by R2Fix could have shortened and saved an average of 68 days of bug diagnosis and patch generation time.

Many bugs are reported during the software maintenance phase, and in order for asoftware product to have a longer life, it must effectively handle and resolve thesebugs. As a result, when cost and time are considered, a prioritized list of bugs isrequired for all products. Due to some factors, such as user expertise, the numberof bugs, the priority methodology, and how critical the software is, developing a prioritization technique that includes user inputs and preset bug constraints to producea final prioritization list of software bugs is challenging. Our approach to solvingthe prioritization problem involves combining an SMT solver with user interactionto provide the best possible solution. Our findings suggest that this strategy outperforms both random and non-interactive bug prioritization methods.

One of the more promising solutions for automated binary testing today is hybrid fuzzing, a combination of the two acknowledged approaches, fuzzing and symbolic execution, for detecting errors in code. Hybrid fuzzing is one of the pioneering works coming from the authors of Angr and Driller, opening up for the possibility for more specialized tools such as QSYM to come forth. These hybrid fuzzers are coverage guided, meaning they measure their success in how much code they have covered. This is a typical approach but, as with many, it is not flawless. Just because a region of code has been covered does not mean it has been fully tested. Some flaws depend on the context in which the code is being executed, such as double-free vulnerabilities. Even if the free routine has been invoked twice, it does not mean that a double-free bug has occurred. To cause such a vulnerability, one has to free the same memory chunk twice (without it being reallocated between the two invocations to free). In this research, we will extend one of the current state-of-the-art hybrid fuzzers, QSYM, which is an open source project. We do this extension, adding double-free detection, in a tool we call QSIMP. We will then investigate our hypothesis, stating that it is possible to implement such functionality without losing so much performance that it would make the tool impractical. To test our hypothesis we have designed two experiments. One experiment tests the ability of our tool to find double-free bugs (the type of context-sensitive bug that we have chosen to test with). In our second experiment, we explore the scalability of the tool when this functionality is executed. Our experiments showed that we were able to implement context-sensitive bug detection within QSYM. We can find most double-free vulnerabilities we have tested it on, although not all, because of some optimizations that we were unable to build past. This has been done with small effects on scalability according to our tests. Our tool can find the same bugs that the original QSYM while adding functionality to find double-free vulnerabilities. / En av de mer lovande lösningarna för automatiserad binärtestning är i dagsläget hybrid fuzzing, en kombination av två vedertagna tillvägagångssätt, fuzzing och symbolisk exekvering. Forskarna som utvecklade Angr och Driller anses ofta vara några av de första med att testa denna approach. Detta har i sin tur öppnat upp för fler mer specialiserade verktyg som QSYM. Dessa hybrid fuzzers mäter oftast sin framgång i hänsyn till hur mycket kod som nås under testningen. Detta är ett typiskt tillvägagångssätt, men som med många metoder är det inte felfri. Kod som har exekverats, utan att en bugg utlösts, är inte nödvändigtvis felfri. Vissa buggar beror på vilken kontext maskininstruktioner exekveras i -- ett exempel är double-free sårbarheter. Att minne har frigjorts flera gånger betyder inte ovillkorligen att en double-free sårbarhet har uppstått. För att en sådan sårbarhet ska uppstå måste samma minne frigöras flera gånger (utan att detta minne omallokerats mellan anropen till free). I detta projekt breddar vi en av de främsta hybrid fuzzers, QSYM, ett projekt med öppen källkod. Det vi tillför är detektering av double-free i ett verktyg vi kallar QSIMP. Vi undersöker sedan vår hypotes, som säger att det är möjligt att implementera sådan funktionalitet utan att förlora så mycket prestanda att det gör verktyget opraktiskt. För att bepröva hypotesen har vi designat två experiment. Ett experiment testar verktygets förmåga att detektera double-free sårbarheter (den sortens kontext-känsliga sårbarheter vi har valt att fokusera på). I det andra experimentet utforskar vi huruvida verktyget är skalbart då den nya funktionaliteten körs. Våra experiment visar att vi har möjliggjort detektering av kontext-känsliga buggar genom vidareutveckling av verktyget QSYM. QSIMP hittar double-free buggar, dock inte alla, på grund av optimiseringar som vi ej har lyckats arbeta runt. Detta har gjorts utan större effekter på skalbarheten av verktyget enligt resultaten från våra experiment. Vårt verktyg hittar samma buggar som orignal verktyget QSYM, samtidigt som vi tillägger funktionalitet för att hitta double-free sårbarheter.