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Rust is a relatively new programming language that targets efficient and safe systems-level applications. It includes a sophisticated type system that allows for provable memory- and thread-safety, and is explicitly designed to take the place of unsafe languages such as C and C++ in the coding ecosystem. There is a large existing C and C++ codebase (many of which have been affected by bugs and security vulnerabilities due to unsafety) that would benefit from being rewritten in Rust to remove an entire class of potential bugs. However, porting these applications to Rust manually is a daunting task. In this paper we investigate the problem of automatically translating C programs into safer Rust programs--that is, Rust programs that improve on the safety guarantees of the original C programs. We conduct an in-depth study into the underlying causes of unsafety in translated programs and the relative impact of fixing each cause. We also describe a novel technique for automatically removing a particular cause of unsafety and evaluate its effectiveness and impact. This paper presents the first empirical study of unsafety in translated Rust programs (as opposed to programs originally written in Rust) and also the first technique for automatically removing causes of unsafety in translated Rust programs.

In this paper we address the challenge of cross-language clone detection. Due to the rise of cross-language libraries and applications (e.g., apps written for both Android and iPhone), it has become common for code fragments in one language to be ported over into another language in an extension of the usual "copy and paste" coding methodology. As with single-language clones, it is important to be able to detect these cross-language clones. However there are many real-world crosslanguage clones that existing techniques cannot detect. We describe the first general, cross-language algorithm that combines both structural and nominal similarity to find syntactic clones, thereby enabling more complete clone detection than any existing technique. This algorithm also performs comparably to the state of the art in singlelanguage clone detection when applied to single-language source code; thus it generalizes the state of the art in clone detection to detect both single-and cross-language clones using one technique.

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The rapid expansion of the human population has raised the chemical stress on the environment due to the increased demand of agricultural yields. The use of pesticides is the primary contributor to environmental chemical stress, which is essential for agricultural expansion in order to produce enough food to sustain the burgeoning human population. Pesticide residues in soil have grown to be a subject of rising concern as a result of their high soil retention and potential harm to unintended species. Diverse remediation strategies, such as physical, chemical, and biological, for limiting and getting rid of such contaminants have been put forth to deal with this problem. Bioremediation is one of these techniques, which has been deemed the best for reducing pollution because of its low environmental impact, simplicity of operation and construction. Microorganisms are implemented in this technique to break down and get rid of toxins in the environment or to reduce the toxicity of chemical compounds. This study thoroughly analyses the different composting soil remediation methods, including landfarming, biopiles, and windrows, to reduce and eliminate soil pollution. Although biological treatment is the best option for cleaning up polluted soil, it is still important to evaluate and review the approaches over the long term to determine whether they are effective in the field. It is because the reactivity of the microorganisms is highly dependent on environmental parameters, and the contemporary environment is characterised by unpredictable weather patterns, localised droughts, and temperature fluctuations.