Fault Localization in Multi-Threaded C Programs using Bounded Model Checking (extended version)

Software debugging is a very time-consuming process, which is even worse for multi-threaded programs, due to the non-deterministic behavior of thread-scheduling algorithms. However, the debugging time may be greatly reduced, if automatic methods are used for localizing faults. In this study, a new method for fault localization, in multi-threaded C programs, is proposed. It transforms a multi-threaded program into a corresponding sequential one and then uses a fault-diagnosis method suitable for this type of program, in order to localize faults. The code transformation is implemented with rules and context switch information from counterexamples, which are typically generated by bounded model checkers. Experimental results show that the proposed method is effective, in such a way that sequential fault-localization methods can be extended to multi-threaded programs.Comment: extended version of paper published at SBESC'1

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

Automatic source code repair methods

Tässä työssä tutustutaan erilaisiin automaattiseen ohjelmakoodiin virheiden korjauksen työkaluihin, sekä yleistetään niiden taustalla olevia toimintamenetelmiä. Tavoitteena on selvittää, millaisia menetelmiä automaattisessa virheenkorjauksessa on kahden viime vuoden aikana käytetty. Lisäksi tutkitaan, kuinka suosittuja eri menetelmät ovat. Työ on kirjallisuuskatsaus, jossa käytiin läpi 16 vuodesta 2015 alkaen julkaistua työkalua. Jokaisen työkalun käyttämään toimintatapaan tutustuttiin, ja ne ryhmiteltiin kuuteen toisistaan eroavaan korjausmenetelmään. Näiden menetelmien yleisyyttä tutkittujen työkalujen joukossa vertailtiin toisiinsa. Myös menetelmien yhdistelyä työkaluissa selvitettiin. Kuudesta määritellystä korjausmenetelmästä yleisimmät ovat korjaussynteesi ja geneettinen ohjelmointi. Näistä ensimmäistä käytti kahdeksan työkalua ja toista viisi. Muut korjaustavat työssä esiteltävät menetelmät ovat symbolinen suoritus, kehityshistorian louhinta, koodinsiirto ja valmiit korjausmallit. Yksittäinen korjaustyökalu käyttää joko yhtä tai kahta menetelmää toiminnassaan. Yleisiksi menetelmien rajat ylittäviksi piirteiksi korjausohjelmissa havaittiin yleiskäyttöisyys ja testeihin perustuva toiminta. Korjaukset jaetaan usein semanttisiin ja syntaktisiin. Jakoa käytettiin myös tässä työssä, minkä lisäksi kehitettiin toinen kahtiajako korjatun ohjelmakoodin lähteen perusteella. Uudessa jaossa korjausmenetelmä on joko koodia luova tai koodia hakeva. Määritellyt menetelmät asetettiin näista kahdesta jaosta luotuun nelikenttään. Lopputuloksena on helposti omaksuttava jäsennys menetelmien suurista eroista

Automated Fixing of Programs with Contracts

This paper describes AutoFix, an automatic debugging technique that can fix faults in general-purpose software. To provide high-quality fix suggestions and to enable automation of the whole debugging process, AutoFix relies on the presence of simple specification elements in the form of contracts (such as pre- and postconditions). Using contracts enhances the precision of dynamic analysis techniques for fault detection and localization, and for validating fixes. The only required user input to the AutoFix supporting tool is then a faulty program annotated with contracts; the tool produces a collection of validated fixes for the fault ranked according to an estimate of their suitability. In an extensive experimental evaluation, we applied AutoFix to over 200 faults in four code bases of different maturity and quality (of implementation and of contracts). AutoFix successfully fixed 42% of the faults, producing, in the majority of cases, corrections of quality comparable to those competent programmers would write; the used computational resources were modest, with an average time per fix below 20 minutes on commodity hardware. These figures compare favorably to the state of the art in automated program fixing, and demonstrate that the AutoFix approach is successfully applicable to reduce the debugging burden in real-world scenarios.Comment: Minor changes after proofreadin

Genetic Improvement of Software: a Comprehensive Survey

Genetic improvement (GI) uses automated search to find improved versions of existing software. We present a comprehensive survey of this nascent field of research with a focus on the core papers in the area published between 1995 and 2015. We identified core publications including empirical studies, 96% of which use evolutionary algorithms (genetic programming in particular). Although we can trace the foundations of GI back to the origins of computer science itself, our analysis reveals a significant upsurge in activity since 2012. GI has resulted in dramatic performance improvements for a diverse set of properties such as execution time, energy and memory consumption, as well as results for fixing and extending existing system functionality. Moreover, we present examples of research work that lies on the boundary between GI and other areas, such as program transformation, approximate computing, and software repair, with the intention of encouraging further exchange of ideas between researchers in these fields

Технології автоматичного виправлення помилок безпеки в програмному забезпеченні

Обсяг роботи 96 сторінок, 24 ілюстрації, 23 таблиці, 88 джерел літератури. Об'єктом дослідження є вразливе програмне забезпечення, що містить помилки безпеки. Предметом дослідження є методи аналізу проміжного представлення коду, методи глибинного навчання для пошуку вразливостей та методи автоматичного виправлення помилок безпеки в коді програмного забезпечення. Методи дослідження - представлення початкового коду у вигляді абстрактного синтаксичного дерева, методи глибинного навчання, що дозволяють генерувати виправлення для помилок безпеки. Наукова новизна полягає в тому, що отримав подальший розвиток метод виправлення помилок безпеки в програмному забезпеченні написаному мовою програмування С/С++ на основі детермінованих правил шляхом додавання специфічних шаблонів, що автоматично трансформують абстрактне синтаксичне дерево виправляючи відповідну помилку безпеки. Також отримав подальший розвиток метод виправлення помилок безпеки на основі глибинного навчання шляхом попередньої обробки коду для підвищення точності завдяки видобуванню найбільш істотних ознак для помилки безпеки. Результати роботи викладені у третьому розділі, що демонструють роботу систем виправлення помилок безпеки на основі детермінованих шаблонів та на основі глибинного навчання. Результати роботи можуть бути використані виправлення специфічних помилок безпеки в початковому коді програмного забезпечення.The volume of work is 96 pages, 24 illustrations, 23 tables, 88 sources of literature. The object of the study is vulnerable software that contains security issues. The subject of the study is methods of analysis of the intermediate code representation, methods of deep learning to find vulnerabilities and methods of automatic patch generation for security issues in software. Research methods - presenting the source code in the form of an abstract syntax tree, deep learning methods that allow you to generate patches for security issues. The scientific novelty is that the method of correcting security errors in software written in C / C ++ programming language based on deterministic rules has been further developed by adding specific templates that automatically transform the abstract syntax tree by correcting the corresponding security error. A method of correcting security errors based on deep learning has also been further developed by pre-processing code to improve accuracy by extracting the most essential features for a security error. The results of the work are presented in Section 3, which demonstrates the performance of security-based path generation systems based on deterministic patterns and deep learning. The results of the work can be used to generate patches for specific security issues in the source code of the software

Automated Debugging using Path-Based Weakest Preconditions

Abstract. Software debugging is the activity of locating and correcting erroneous statements in programs. Automated tools to locate and correct the erroneous statements in a program can significantly reduce the cost of software development. In this paper, we present a new approach to locate and correct an erroneous statement in a function. We assume the correct specification of the erroneous function is available in the form of preconditions and postconditions of the function. Our approach combines ideas from software testing and weakest preconditions used in correctness proof methods to locate a likely erroneous statement. We have implemented our approach and conducted experiments with several small programs. In our experiments, our approach was able to locate the erroneous statements in a large number of cases. Our preliminary experimental results show that our approach has potential for development of an automated bug location and correction tool

Automatic software generation and improvement through search based techniques

Writing software is a difficult and expensive task. Its automation is hence very valuable. Search algorithms have been successfully used to tackle many software engineering problems. Unfortunately, for some problems the traditional techniques have been of only limited scope, and search algorithms have not been used yet. We hence propose a novel framework that is based on a co-evolution of programs and test cases to tackle these difficult problems. This framework can be used to tackle software engineering tasks such as Automatic Refinement, Fault Correction and Improving Non-functional Criteria. These tasks are very difficult, and their automation in literature has been limited. To get a better understanding of how search algorithms work, there is the need of a theoretical foundation. That would help to get better insight of search based software engineering. We provide first theoretical analyses for search based software testing, which is one of the main components of our co-evolutionary framework. This thesis gives the important contribution of presenting a novel framework, and we then study its application to three difficult software engineering problems. In this thesis we also give the important contribution of defining a first theoretical foundation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo