Automatic assessment of Java code

[EN] Assessment is an integral part of education often used to evaluate students, but also to provide them with feedback. It is essential to ensure that assessment is fair, objective, and equally applied to all students. This holds, for instance, in multiple-choice tests, but, unfortunately, it is not ensured in the assessment of source code, which is still a manual and error-prone task. In this paper, we present JavAssess, a Java library with an API composed of around 200 methods to automatically inspect, test, mark, and correct Java code. It can be used to produce both black-box (based on output comparison) and white-box (based on the internal properties of the code) assessment tools. This means that it allows for marking the code even if it is only partially correct. We describe the library, how to use it, and we provide a complete example to automatically mark and correct a student's code. We also report the use of this system in a real university context to compare manual and automatic assessment in university courses. The study reports the average error in the marks produced by teachers when assessing source code manually, and it shows that the system automatically assesses around 50% of the work. (C) 2018 Elsevier Ltd. All rights reserved.This work has been partially supported by MINECO/AEI/FEDER (EU) under grants TIN2013-44742-C4-1-R and TIN2016-76843-C4-1-R, by the Generalitat Valenciana under grant PROMETEO-II/2015/013 (SmartLogic), and by the Universitat Politecnica de Valencia under grant PIME B18 and EICE HEGEA.Insa Cabrera, D.; Silva, J. (2018). Automatic assessment of Java code. Computer Languages Systems & Structures. 53:59-72. https://doi.org/10.1016/j.cl.2018.01.004S59725

Semi-automatic assessment of unrestrained Java code: a Library, a DSL, and a workbench to assess exams and exercises

© ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in http://dx.doi.org/10.1145/2729094.2742615Automated marking of multiple-choice exams is of great interest in university courses with a large number of students. For this reason, it has been systematically implanted in almost all universities. Automatic assessment of source code is however less extended. There are several reasons for that. One reason is that almost all existing systems are based on output comparison with a gold standard. If the output is the expected, the code is correct. Otherwise, it is reported as wrong, even if there is only one typo in the code. Moreover, why it is wrong remains a mystery. In general, assessment tools treat the code as a black box, and they only assess the externally observable behavior. In this work we introduce a new code assessment method that also verifies properties of the code, thus allowing to mark the code even if it is only partially correct. We also report about the use of this system in a real university context, showing that the system automatically assesses around 50% of the work.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Economíay Competitividad (Secretaría de Estado de Investigación, Desarrollo e Innovación) under grant TIN2013-44742-C4-1-R and by the Generalitat Valenciana under grant PROMETEOII2015/013. David Insa was partially supported by the Spanish Ministerio de Educación under FPU grant AP2010-4415.Insa Cabrera, D.; Silva, J. (2015). Semi-automatic assessment of unrestrained Java code: a Library, a DSL, and a workbench to assess exams and exercises. ACM. https://doi.org/10.1145/2729094.2742615SK. A Rahman and M. Jan Nordin. A review on the static analysis approach in the automated programming assessment systems. In National Conference on Programming 07, 2007.K. Ala-Mutka. A survey of automated assessment approaches for programming assignments. In Computer Science Education, volume 15, pages 83--102, 2005.C. Beierle, M. Kula, and M. Widera. Automatic analysis of programming assignments. In Proc. der 1. E-Learning Fachtagung Informatik (DeLFI '03), volume P-37, pages 144--153, 2003.J. Biggs and C. Tang. Teaching for Quality Learning at University : What the Student Does (3rd Edition). In Open University Press, 2007.P. Denny, A. Luxton-Reilly, E. Tempero, and J. Hendrickx. CodeWrite: Supporting student-driven practice of java. In Proceedings of the 42nd ACM technical symposium on Computer science education, pages 09--12, 2011.R. Hendriks. Automatic exam correction. 2012.P. Ihantola, T. Ahoniemi, V. Karavirta, and O. Seppala. Review of recent systems for automatic assessment of programming assignments. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research, pages 86--93, 2010.H. Kitaya and U. Inoue. An online automated scoring system for Java programming assignments. In International Journal of Information and Education Technology, volume 6, pages 275--279, 2014.M.-J. Laakso, T. Salakoski, A. Korhonen, and L. Malmi. Automatic assessment of exercises for algorithms and data structures - a case study with TRAKLA2. In Proceedings of Kolin Kolistelut/Koli Calling - Fourth Finnish/Baltic Sea Conference on Computer Science Education, pages 28--36, 2004.Y. Liang, Q. Liu, J. Xu, and D. Wang. The recent development of automated programming assessment. In Computational Intelligence and Software Engineering, pages 1--5, 2009.K. A. Naudé, J. H. Greyling, and D. Vogts. Marking student programs using graph similarity. In Computers & Education, volume 54, pages 545--561, 2010.A. Pears, S. Seidman, C. Eney, P. Kinnunen, and L. Malmi. Constructing a core literature for computing education research. In SIGCSE Bulletin, volume 37, pages 152--161, 2005.F. Prados, I. Boada, J. Soler, and J. Poch. Automatic generation and correction of technical exercices. In International Conference on Engineering and Computer Education (ICECE 2005), 2005.M. Supic, K. Brkic, T. Hrkac, Z. Mihajlovic, and Z. Kalafatic. Automatic recognition of handwritten corrections for multiple-choice exam answer sheets. In Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 1136--1141, 2014.S. Tung, T. Lin, and Y. Lin. An exercise management system for teaching programming. In Journal of Software, 2013.T. Wang, X. Su, Y. Wang, and P. Ma. Semantic similarity-based grading of student programs. In Information and Software Technology, volume 49, pages 99--107, 2007

Mathematics base for navigation mobile robot using reachability petri net

Navigation is the journey that brings the precision and accuracy especially for the trip from one place to many destinations. Traveling mobile robot will be challenged when there are plenty of travel options and obstacles; it is necessary for special methods to deal with this. Petri net modeling is one method that can be applied in this navigation. As a model network graph, Petri net can be seen as a model of superior visuals for their token moves that define the process dynamics of a system. Other models that have been provided, such as flow charts, block diagrams or network topology, are using the method of mathematical reachability. Meanwhile Petri net obtained the navigation as a result of marking the value. The final results of this research is that Petri net can be used to obtain mobile robot for navigation

Automated Feedback for 'Fill in the Gap' Programming Exercises

Timely feedback is a vital component in the learning process. It is especially important for beginner students in Information Technology since many have not yet formed an effective internal model of a computer that they can use to construct viable knowledge. Research has shown that learning efficiency is increased if immediate feedback is provided for students. Automatic analysis of student programs has the potential to provide immediate feedback for students and to assist teaching staff in the marking process. This paper describes a “fill in the gap” programming analysis framework which tests students’ solutions and gives feedback on their correctness, detects logic errors and provides hints on how to fix these errors. Currently, the framework is being used with the Environment for Learning to Programming (ELP) system at Queensland University of Technology (QUT); however, the framework can be integrated into any existing online learning environment or programming Integrated Development Environment (IDE

Automatic Repair of Real Bugs: An Experience Report on the Defects4J Dataset

Defects4J is a large, peer-reviewed, structured dataset of real-world Java bugs. Each bug in Defects4J is provided with a test suite and at least one failing test case that triggers the bug. In this paper, we report on an experiment to explore the effectiveness of automatic repair on Defects4J. The result of our experiment shows that 47 bugs of the Defects4J dataset can be automatically repaired by state-of- the-art repair. This sets a baseline for future research on automatic repair for Java. We have manually analyzed 84 different patches to assess their real correctness. In total, 9 real Java bugs can be correctly fixed with test-suite based repair. This analysis shows that test-suite based repair suffers from under-specified bugs, for which trivial and incorrect patches still pass the test suite. With respect to practical applicability, it takes in average 14.8 minutes to find a patch. The experiment was done on a scientific grid, totaling 17.6 days of computation time. All their systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair

You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems

Properly benchmarking Automated Program Repair (APR) systems should contribute to the development and adoption of the research outputs by practitioners. To that end, the research community must ensure that it reaches significant milestones by reliably comparing state-of-the-art tools for a better understanding of their strengths and weaknesses. In this work, we identify and investigate a practical bias caused by the fault localization (FL) step in a repair pipeline. We propose to highlight the different fault localization configurations used in the literature, and their impact on APR systems when applied to the Defects4J benchmark. Then, we explore the performance variations that can be achieved by `tweaking' the FL step. Eventually, we expect to create a new momentum for (1) full disclosure of APR experimental procedures with respect to FL, (2) realistic expectations of repairing bugs in Defects4J, as well as (3) reliable performance comparison among the state-of-the-art APR systems, and against the baseline performance results of our thoroughly assessed kPAR repair tool. Our main findings include: (a) only a subset of Defects4J bugs can be currently localized by commonly-used FL techniques; (b) current practice of comparing state-of-the-art APR systems (i.e., counting the number of fixed bugs) is potentially misleading due to the bias of FL configurations; and (c) APR authors do not properly qualify their performance achievement with respect to the different tuning parameters implemented in APR systems.Comment: Accepted by ICST 201

Structured Review of the Evidence for Effects of Code Duplication on Software Quality

This report presents the detailed steps and results of a structured review of code clone literature. The aim of the review is to investigate the evidence for the claim that code duplication has a negative effect on code changeability. This report contains only the details of the review for which there is not enough place to include them in the companion paper published at a conference (Hordijk, Ponisio et al. 2009 - Harmfulness of Code Duplication - A Structured Review of the Evidence)