Background. Malware has been a major issue for years and old signature scanning methods for detecting malware are outdated and can be bypassed by most advanced malware. With the help of machine learning, patterns of malware behavior and structure can be learned to detect the more advanced threats that are active today. Objectives. In this thesis, research to find state-of-the-art machine learning methods to detect malware is proposed. A dataset collection method will be found in research to be used in an experiment. Three selected methods will be re-implemented for an experiment to compare which has the best performance. All three algorithms will be trained and tested on the same dataset. Methods. A literature review with the snowballing technique was proposed to find the state-of-the-art detection methods. The malware was collected through the malware database VirusShare and the total number of samples was 14924. The algorithms were re-implemented, trained, tested, and compared by accuracy, true positive, true negative, false positive, and false negative. Results. The results showed that the best performing research available are image detection, N-Gram combined with meta-data and Function Call Graphs. However, a new method was proposed called Running Window Entropy which does not have a lot of research about it and still can achieve decent accuracy. The selected methods for comparison were image detection, N-Gram, and Running Window Entropy where the results show they had an accuracy of 94.64%, 96.45%, and 93.71% respectively. Conclusions. On this dataset, it showed that the N-Gram had the best performance of all three methods. The other two methods showed that, depending on the use case, either can be applicable.

Static analysis tools can be used to help programmers identify problems in their code. However, these tools often assume that developers have some programming background knowledge, so they can be hard to use in an educational context. We investigated the most common FindBugs errors from student code submissions and determined those errors that were related to incorrect solutions to problems and potential struggling for students. FindBugs is a static analysis tool that looks for incorrect patterns in Java bytecode analysis to identify potential coding flaws. For the common errors, we rewrote some of the original FindBugs messages to help students more easily understand the problems with their code. We found that students with at least one FindBugs warning in their final submission to an assignment had more submissions, longer work times, and lower correctness scores than students who did not have a FindBugs warning in their final submission. Adding modified FindBugs feedback to the automated grader resulted in students making fewer submissions and decreasing the length of time required to complete assignments. / Master of Science / Professional software developers use automated tools when they code to help them catch potential coding problems. These tools are difficult for novice student programmers because they do not have the same level of background as professionals. In this work, we attempted to change the feedback given by these tools so that students could understand it and use it to fix their code. We found that, across all of the undergraduate courses in this study, FindBugs warnings were associated with students having more trouble with assignments. When students could see FindBugs warnings, their time to complete assignments and the number of attempts they made both went down.

In this investigation, the elasto-plastic behavior and the seismic performance of concrete reinforced frame structures reinforced are evaluated by applying the Pushover method. This evaluation is done on several cases: with high ductility steel (Grade 40), conventional steel (Grade 60) and high strength steel (Grade 75). For the previous, the capacity curve graph obtained from the displacement coefficient method was used to measure the capacity of the structure. In addition, the performance of the structure for different levels of seismic design are evaluated with the resulting values of ductility and rigidity of each case. The results showed that reinforcing a structure with a Grade 40 reinforcing steel increases the energy dissipation capacity, and if reinforced with a Grade 75 reinforcing steel increases the strength capacity in the structure. Finally, the comparative result of the various cases are presented to demonstrate the influence of reinforcing steel on the plastic behavior of concrete reinforced frame structures.

No description available.

The quality and reliability of software systems, in terms of their functional correctness, critically relies on the effectiveness of the testing tools and techniques to detect errors in the system before deployment. A lack of testing tools for concurrent programs that systematically control thread scheduling choices has not allowed concurrent software development to keep abreast with hardware trends of multi-core and multi-processor technologies. This motivates a need for the development of systematic testing techniques that detect errors in concurrent programs. The work in this dissertation presents a potentially scalable technique that can be used to detect concurrency errors in production code. The technique is a viable solution for software engineers and testers to detect errors in multi-threaded programs before deployment. We present a guided testing technique that combines static analysis techniques, systematic verification techniques, and heuristics to efficiently detect errors in concurrent programs. An abstraction-refinement technique lies at the heart of the guided test technique. The abstraction-refinement technique uses as input potential errors in the program generated by imprecise, but scalable, static analysis tools. The abstraction further leverages static analyses to generate a set of program locations relevant in verifying the reachability of the potential error. Program execution is guided along these points by ranking both thread and data non-determinism. The set of relevant locations is refined when program execution is unable to make progress. The dissertation also discusses various heuristics for effectively guiding program execution. We implemented the guided test technique to detect errors in Java programs. Guided test successfully detects errors caused by thread schedules and data input values in Java benchmarks and the JDK concurrent libraries for which other state of the art analysis and testing tools for concurrent programs are unable to find an error.

No description available.

No description available.

Calculating WCET for schedulability analysis of RTIC applications is today performed with a hybrid approach with both static analysis of code and hardware measurements. A fully static analysis tool would allow for a easier integration into a CI/CD pipeline without the actual hardware. This thesis attempts to compute WCET statically, using symbolic execution engine KLEE to generate all the possible paths of execution for a task and then analyses these paths to approximate the worst-case for each path which would yield a approximate WCET for the analysed program. To analyze a path in a program the low-level intermediary assembly language used by the LLVM optimization infrastructure (called LLVM IR) is compared to the finished assembly language to draw conclusions on how an LLVM IR instruction is processed into assembly. To be able to perform this mapping from LLVM IR to assembly, the symbolic execution engine KLEE has been extended to also log each LLVM IR instruction run in a path. These logs combined with a translation table is how the approximations are calculated. The resulting approximations correlate with the actual cycles when the analysed program is run on actual hardware, which indicates that tool could actually be used to approximate WCET. There are however no guarantees and the tool has not been tested for larger scale programs.

Utilizing immutability is considered to have many desired benefits when it comes to software development and reasoning about programs. It is also one of the core principles of functional programming, and many programming languages have support for specifying immutability. Developers can by specifying immutability write code that, for example, prevent program state from being unintentionally mutated. The Scala programming language is a functional and object-oriented language where developers can specify immutability with reassignable and non-reassignable variables. The type system in Scala has no built-in support for developers to express the fact that a type is immutable, immutability is instead by convention and considered best practice. However, knowledge about the immutability usage and how prevalent it is in real-world Scala code are until this point non-existent.            This project presents an immutability analysis and evaluation of six small-to-large open source projects written in Scala providing empirical data on immutability usage. The analysis investigates the immutability property of templates, where a template refers to one of Scala's different class types, on three distinct properties: shallow, conditionally deep and deep immutability, where deep is the strongest immutability property. The analysis works as a plug-in for the Scala compiler that statically analyzes the source code of projects. We report immutability statistics for each evaluated project, including three widely used projects, Scala's standard library, Akka's actor framework and ScalaTest. Explanations to why stronger immutability properties do not hold are also provided. The analysis show that the majority of templates for each project satisfied an immutability property and were not classified as mutable. Because each analyzed project had templates that were assumed to be mutable, as they were unreachable by our analysis, there could potentially be more templates that satisfy an immutability property. Inheritance is shown to be an important factor when it comes to a template's immutability and mutability was found to be lower for the template types case class and singleton object. This can be seen as intended by the designers of Scala, indicating that these type of class abstractions help programmers utilize immutability. Our results show that immutability is frequently used in Scala and the high degree of immutability usage could be due to the functional nature of the language. / Att använda immuterbar (oföränderlig) data anses ha många önskvärda fördelar när det kommer till utveckling av program och att kunna resonera om dess funktionalitet. Immuterbar data är också en viktig princip inom funktionell programmering och många språk har idag stöd för att ange immuterbarhet. Utvecklare kan i kod ange ifall data ska vara immuterbar för att till exempel förhindra ett programtillstånd från att oavsiktligt förändras. Programmeringsspråket Scala är ett funktionellt och objektorienterat språk där utvecklare kan ange immuterbarhet med två typer av variabler, en som är tilldelningsbar och en som är icke-tilldelningsbar. Typsystemet i Scala har inget inbyggt stöd för utvecklare att uttrycka det faktum att en typ är immuterbar, att använda immuterbarhet är i stället konvention och anses vara den bästa metoden. Men uppgifter om hur immuterbarhet egentligen används i riktiga Scala projekt har fram tills nu inte varit tillgängligt. Detta projekt presenterar en immuterbarhetsanalys och en utvärdering av sex små till stora projekt med öppen källkod skrivna i programmeringsspråket Scala. Analysen undersöker immuterbarhetsegenskaper hos Scalas olika typer av klasser med avseende på tre olika egenskaper: ytlig, villkorligt djup och djup immuterbar, där djup är den starkaste immuterbarhetsegenskapen. Analysen fungerar som ett tillägg för Scalas kompilator och utfärdar en statisk analys av källkoden för ett projekt. Statistik om immuterbarhet för varje projekt redovisas och utvärderas, bland annat tre välkända och populära kodbaser, Scalas standard bibliotek, Akka’s actor ramverk och ScalaTest. Förklaringar till varför klasser inte uppfyller en immuterbarhetsegenskap visas också. Analysen visar att majoriteten av alla klasser i projekten har en immuterbarhetsegenskap och var inte klassificerade som muterbara. Eftersom varje projekt hade klasser som antogs vara muterbara för att dessa inte var nåbara för våran analys så kan det potentiellt finnas fler klasser som har en immuterbarhetsegenskap. Vad en klass ärver visar sig vara en viktig faktor när det kommer till vilken typ av immuterbarhetsegenskap den har. Muterbarhet visade sig vara lägre för klasser som är av typen case class and singleton object vilket kan anses vara avsett av Scalas skapare, då dessa klass abstraktioner hjälper programmerare att använda immuterbarhet. Resultaten visar att immuterbarhet används flitigt i Scala och den höga användningsgraden kan vara på grund av att det är ett funktionellt språk.

<p> An oblique four bar structural model with fixed member ends, being the most general building block for space frames, is analysed for establishing its influence coefficients, using the Finite Element Matrix Method. </p> <p> Experimental techniques for measurement of the flexibility influence coefficients of the model are described. </p> <p> Experimental results have been compared against analytical ones. </p> / Thesis / Master of Engineering (MEngr)