Efficient and linear static approach for finding the memory leak in C

Code analysis has discovered that memory leaks are common in the C programming language. In the literature, there exist various approaches for statically analyzing and detecting memory leaks. The complexity and diversity of memory leaks make it difficult to find an approach that is both effective and simple. In embedded systems, costly resources like memory become limited as the system’s size diminishes. As a result, memory must be handled effectively and efficiently too. To obtain precise analysis, we propose a novel approach that works in a phase-wise manner. Instead of examining all possible paths for finding memory leaks, we use a program slicing to check for a potential memory leak. We introduce a source-sink flow graph (SSFG) based on source-sink properties of memory allocation-deallocation within the C code. To achieve simplicity in analysis, we also reduce the complexity of analysis in linear time. In addition, we utilize a constraint solver to improve the effectiveness of our approach. To evaluate the approach, we perform manual scanning on various test cases: link list applications, Juliet test cases, and common vulnerabilities and exposures found in 2021. The results show the efficiency of the proposed approach by preparing the SSFG with linear complexity

Performance optimization of a Java instrumentation agent for calling context encoding

Funktsioonikutsete ajalugu, mida kasutajale trükitakse pinujäljena, on suureks abiks programmis toimuva vea täpse asukoha leidmiseks lähtekoodis. Sügavamate probleemide puhul on aga vaja programmi täitmist pikemalt jälgida ja oluliste sündmuste toimumisel nende funktsioonikutsete ajalugu salvestada. Kuna terve ajalugu on väga pikk, siis on mõistlik seda kodeerida. Selles magistritöös uuritakse ühte konkreetset kodeerimise algoritmi Lucce, tuues välja nii selle eeliseid teiste algoritmidega võrreldes kui ka probleeme jõudlusega. Eesmärgiks on selle algoritmi jõudlust tõsta ja sellel näitel tutvustada üldiseid ning Java agentidega seotud jõudluse tõstmise võtteid.The idea behind calling context encoding algorithms is to efficiently build a call graph of an application in order to be able to give developers a call stack trace of any event at any point of the program execution. Having the information that calling context provides ena-bles developers to better interpret results of monitoring and profiling tools. In this paper, we discuss in greater detail the benefits of calling context encoding and the problems with current algorithms that are trying to construct calling context. We take an algorithm im-plemented as Java instrumentation agent - Lucce - and explain its promising possibilities, benefits over other similar algorithms, as well as its main performance problem. This thesis contributes to this field firstly by presenting an analysis of different methods of perfor-mance optimization and their applications to a Java agent, and secondly by applying these methods to the performance optimization of the Lucce algorithm and its Java implementa-tion

Stairway to Excellence. Country Report: Estonia

In the frame of the Stairway to Excellence project, country analysis was performed for the EU MS that joined the EU since 2004, with the objective to assess and corroborate all the qualitative and quantitative data in drawing national/regional FP7 participation patterns, understand the push–pull factors for FP7/H2020 participation and the factors affecting the capacity to absorb cohesion policy funds. This report articulates analysis on selected aspects and country-tailored policy suggestions aiming to tackle the weaknesses identified in the analysis. The report complements the complex qualitative/ quantitative analysis performed by the IPTS/KfG/S2E team. In order to avoid duplication and cover all the elements required for a sound analysis, the report builds on analytical framework developed by IPTSJRC.J.2-Knowledge for Growt